phd scientific computing germany

Special Conditions

Conditional admission is possible; special conditions may comprise up to 10 credit points (see Examination Regulations § 5 para 2)

Module handbook

• Module handbook: Master Scientific Computing on the website of the study programme

The module handbook provides an overview of the various modules that must be taken in a degree programme. It contains all the important explanations on the requirements and types of module examinations as well as on the intermediate or final examinations and serves as a kind of study guide for orientation.

Examination regulations

• Examination regulations: Master Scientific Computing on the website for official announcements of the University of Bayreuth

Examination regulations define the content and structure of a particular degree programme in a legally binding manner. They must be approved by the Ministry of Science or accredited by an accreditation agency.

• Flyer Master Scientific Computing on the website of the study programme

Contact points in the department

• Programme coordinator: Prof. Dr. Mario Bebendorf
• Student representatives: Student Representatives MPI

Central contact points

• Prospective students: Student Advising  (in German language)
• For international students: International Office
• Examination Office:  Examination Office Faculty I – Mathematics, Physics & Computer Science

Related degree programmes

• Economathematics (M.Sc.)
• Mathematics (M.Sc.)
• Technomathematics (M.Sc.)
• Africa-Competence (AfriZert)
• China-Competence (SinoZert) in German language
• Data Literacy in German language
• Entrepreneurship in German language
• Interculturality Research and Intercultural Practice in German language
• Intersectionality Studies & Diversity Competencies in English language
• Sustainability in German language
• Teaching German as a foreign language in German language
• Theatre Education in German language

Any more questions? Please contact the coordinator of the degree programme.

Prof. Dr. Mario Bebendorf Chair of Scientific Computing

Phone: +49 (0)921 / 55-7150 E-mail: [email protected] Office: Room 0.41, Building Ingenieurwissenschaften (FAN) Universitätsstraße 30, 95447 Bayreuth

Have we aroused your interest? You can find all information about the international Master's Programme Scientific Computing at the University of Bayreuth on the website.

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Scientific Computing, M.Sc.

Program overview.

The Scientific Computing master’s program is a strongly application-oriented program focused on current topics and problems. The program is also very much research focused as students are closely involved in teachers’ research projects.

Students learn how to design holistic solutions for scientific and technical tasks in engineering and natural science applications - from mathematical modeling, analysis of mathematical models, and the development of numeric processes through to the implementation of the processes as software. They also acquire extensive technical knowledge in the field of scientific computing, applied mathematics, and a focus of their choosing. Students may choose, for instance, to focus on numerical mathematics, financial mathematics, or stochastic models.

The curriculum also includes application-oriented content from non-mathematical disciplines. Here, students can choose between physics, chemistry, engineering, biology, and medicine. 

Admission requirements

For the master’s degree program, the applicants must possess a first university degree in mathematics, business mathematics or technomathematics or another degree in a program with sufficient mathematical orientation. Further information can be found in the degree program’s access regulations. The most current version of the regulations applies to applicants.

The Scientific Computing master’s program is taught exclusively in English. Applicants are required to submit proof of English skills at the required level. You can find further information in the study and examination regulations.

Note: Please find the rules for admission under the link for "Study and examination regulations" below.

Program structure

There is a proposed course schedule for the degree program. This is a recommendation for how to complete the degree program within the standard period of study of four semesters. It provides an example of which modules to take in which semesters. While this proposed course schedule is ideal on paper, it is not mandatory. It’s simply an example of how to successfully schedule and shape your studies. You can find the proposed course schedule in the study and examination regulations.

The master program "Scientific Computing" has  new study and exam regulations  which become valid with the  winter semester 2022/2023.

For further information please cf. https://www.math.tu-berlin.de/studienfachberatung_mathematik/master/scientific_computing/parameter/en/

Study and examination regulations:

• Scientific Computing M.Sc. 2021 (pdf, 1 MB, German)
• Scientific Computing M.Sc. 2005 (pdf, 691 KB, German)

Content and modules

The master’s degree program in Scientific Computing consists of modules which combine curriculum content on a specific topic and often include a variety of different study and teaching formats. You can find a module list which offers a current overview of all the modules in TU Berlin’s module transfer system (MTS). In the MTS you have an overview of which modules are mandatory for your degree program and which are elective. Detailed module descriptions provide information about the content, learning objectives, participation requirements, workload, type of assessment, and much more. The module list is based on the study and exam regulations.

Internships

During your studies, you will have an opportunity to complete an optional non-university internship worth 6 credit points. You can find further information in the degree program’s internship regulations.

Stays abroad

The study program structure provides an opportunity for students to complete a stay abroad within the standard period of study. The Faculty has staff to assist you with selecting a university and putting together a schedule. You can obtain general information about stays abroad from the TU Berlin International Office (study abroad) and Career Service (internships abroad).

Acquired skills

The program places a particular focus on allowing students to tailor their individual profile: They further their knowledge and skills in math and apply and develop these in a mathematic sub-discipline that was part of their bachelor’s studies. In the scientific computing component students learn extensive skills in scientific computing and in a profile subject of their choosing, such as numerical mathematics, differential algebraic equations, control theory, or finite element methods to solve differential equations. In the applied mathematics component students gain a comprehensive understanding of, for example, modeling with differential equations, variational calculus and optimal control, stochastic models, non-linear optimization or financial mathematics, depending on which focus they have chosen. Additionally, they have comprehensive expertise in either physics, chemistry, engineering, biology, or medicine. Due to the required research internship, students gain an introduction to application-oriented work in research and development.

After your studies

Our master's graduates find work in a number of areas in industry, business, administration, research institutes, universities, and universities of applied science. Graduates typically find work in mechanical engineering (for example in strength theory and vibration problems), electrical engineering (for instance in regulation engineering, computation of fields, network planning, and communication technology), the chemical industry (such as in reactor calculations and statistical processes), in the aeronautic and aerospace industry (e.g. in fluid calculations and orbit determinations), in civil engineering (e.g. in statics and material stability), in biology and medicine (e.g. epidemic models and diagnostic evaluations), business and economics (e.g. operations research, organization and planning, securities management, and consulting), insurance, and research institutions of all kinds.

Further information & downloads

Guidance and choosing the right degree program: Academic Advising Service

Questions about the degree program: Course Guidance

General questions: Student Info Services

Application and enrollment: Office of Student Affairs - Graduate Admissions

Recognition of previously acquired credits: Examination Board

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• International Max Planck Research School for Computational Biology and Scientific Computing

International Max Planck Research School for Computational Biology & Scientific Computing (IMPRS-CBSC)

The International Max Planck Research School for Computational Biology and Scientific Computing (IMPRS-CBSC) originated in a joint initiative of the Max Planck Institute for Molecular Genetics and Freie Universität Berlin. Building on the existing master’s degree programs in bioinformatics and scientific computing at Freie Universität Berlin, it is integrated into a number of scientific working groups that work in the fields where life sciences (molecular biology, genome research) and theoretical sciences (mathematics, computer science) overlap. The IMPRS-CBSC focuses on issues of mathematics and computer science with regard to sequential analysis, theoretical structural biology, theoretical chemistry and drug design, molecular evolution, genome analysis, and methods of data analysis for functional genome research.

Berlin-Dahlem offers a unique concentration of academic and scientific excellence in this field. This enables participating students to get to know the full spectrum of formal methods and put them into practice in tackling a variety of applied issues.

Read more: www.imprs-cbsc.mpg.de

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International Programmes 2023/2024

Scientific Computing (Elite Network of Bavaria) Scientific Computing

University of bayreuth • bayreuth.

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• Requirements / Registration
• About the university

Elite Network of Bavaria

English, German only on request

1 March to 15 May (winter semester) 1 September to 15 November (summer semester)

The past several years showed that numerical simulations of phenomena in technology and the natural sciences are an essential tool for accelerating development cycles in industry and businesses. While researchers once had to meticulously study the properties of a product on the basis of prototypes, they are now simulated and optimised on computers. Demands for the capabilities of numerical simulation continue to grow with the need for models that are more and more precise, the incorporation of new problem areas such as data analysis (e.g., big data) or artificial intelligence, and parameter-dependent problems and models with uncertain data. This was triggered by the relatively young and forward-looking research area of scientific computing.

The field addresses the entire workflow, including modelling; mathematical, numerical, and statistical analysis; optimisation; the implementation of algorithms on high-performance computers; and the visualisation of results. However, little attention has been paid to training students in this development.

The objective of the international Master’s programme is to provide a specialised range of courses that leads highly qualified, hard-working students towards the development and mathematical analysis of highly efficient numerical methods. It is a crucial point that highly complex problems are brought to a less complex numerical approximation (on parallel computers) via an understanding of their mathematical core. The Master’s programme involves – and is motivated by – several courses in other subject areas (biochemistry, physics, computer science, engineering, climate and environment), in which the simulation of demanding problems plays a crucial role. The programme is geared towards students working at the intersection of mathematics, computer science, data science, and physics. This interdisciplinary approach enables students to achieve and apply their specialised understanding of efficient methods for solving differential and integral equations and analysing large sets of data, and to extend this know-how to other subject areas.

The Master's programme is organised in elective and mandatory modules. The elective modules consist of several courses, from which the participants can choose according to their interests. They have to fulfil a certain amount of credit points in these elective modules.

The following four main areas are includes in the elite Master's programme in Scientific Computing:

• Numerical mathematics (numerical methods for different types of differential equations, approximation methods, optimisation).
• Modelling and simulation of many problems from (bio)physics, (bio)computer science, chemistry, engineering sciences and climate/environmental sciences
• High performance computing (data structures, parallel systems and algorithms)
• Scientific computing (complexity reduction, fast and efficient methods, mesh-free methods, data analysis, quantification of uncertainties, multiscale problems, optimisation methods in machine learning)

Each year, a modelling seminar (summer semester) and a status seminar (winter semester) will be held. Students must attend two of each of these events.

An industrial internship and a practical course on parallel numerical methods deepen the learned methods and algorithms.

One of the modules of the programme is dedicated to key skills, such as lecture and presentation techniques, literature research, teamwork or dealing with foreign-language specialist literature. Students have to attend seminars in this module for a certain amount of time.

As a conclusion of the programme, each student writes a Master's thesis on an individual research project in co-operation with industry, with international experts or under the guidance of a professor of the University of Bayreuth. For this purpose, students receive compensation for travel expenses during their research stays.

At the beginning of the programme, a mentor is provided to every student. This mentor can be chosen among the involved lecturers. With the help of the mentor, the participants of the programme are able to design an individual study plan in accordance with their interests. Furthermore, the mentors act in an advisory capacity in the studies or the research interests of their students and can recommend themes for Master's theses.

More details on the modules and a recommended curriculum can be found on the programme website: https://www.scientific-computing.uni-bayreuth.de/en/module-overview/index.html

The course organisation and the modules were created in corporation with the Elite Network of Bavaria.

A brief overview of the modules can be found in the attached PDF file.

• International guest lecturers
• Specialist literature in other languages
• Language training provided
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• Courses are led with foreign partners
• Projects with partners in Germany and abroad

Industrial internship

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In Germany, students at all higher education institutions pay a semester contribution. This payment (University of Bayreuth: 142.85 EUR per semester) has nothing to do with tuition fees; rather, it covers your contributions to student services and the student government. At the University of Bayreuth (which combines the campus in Bayreuth and our Faculty VII located in Kulmbach), it includes a "semester ticket" that allows you to use public transport in the region.

The cost of living in Germany, e.g. accommodation, food, clothing and recreational activities, is about average compared to other European countries. Living expenses are significantly lower than in countries like Denmark, Luxembourg or Switzerland, but they are rather high compared to countries like Poland, the Czech Republic or Italy. Compared to other large German cities, such as Munich, Berlin or Hamburg, Bayreuth's low cost of living and affordable housing make the city and the region particularly attractive to young people and families. The DAAD website will tell you what living expenses to expect in Germany: https://www.daad.de/deutschland/nach-deutschland/voraussetzungen/en/9198-financing/ .

Scholarships for international students: International students and doctoral researchers have the opportunity to apply to the International Office for a study grant. Limited funding from the State of Bavaria and the DAAD is available for this purpose. The application deadline for the winter semester is 31 August and for the summer semester 28 February . All grants can be awarded for up to two semesters.

Unfortunately, first-semester students cannot be funded, i.e. applications cannot be submitted until the second semester of study for funding starting in the third semester of study. Above all, academic achievements from the previous semester are decisive for the evaluation of the application.

• A Bachelor’s degree in mathematics, computer science, engineering science or physics (or a degree with equivalent content) with a final grade of 1.9 or better
• Sufficient specialised knowledge in  numerical mathematics of at least 16 credits

Certification of proficiency in English at level B2 according to the  Common European Framework of Reference for Languages is required.

Online application through CAMPUSonline You can find further details via this link .

There are many ways for international students to earn money while they study, but there are some restrictions. For more detailed information, please visit the DAAD website .

The University of Bayreuth’s Career Services team provides a central interface between student and professional life. The team offers guidance and support to students of all subjects with regard to starting their careers .

For information regarding the Corona virus , please visit our website and also see https://www.daad.de/en/coronavirus/ .

Accommodation for students Bayreuth and Kulmbach have a number of student dormitories (both private dormitories and dormitories offered by the Association for Student Affairs) and a vast array of private rooms available. Under no circumstances should you assume that you will be assigned a room in the student dormitories! You will need to actively search for a room on your own – either in a private dormitory or on the private market.

More information regarding accommodation for students is available here: https://www.uni-bayreuth.de/en/studies/accomodation/index.html . Please also read the DAAD’s information .

Accommodation for international guests All other international guests are requested to register via the  Welcome Services Database (WelSe) .

Accommodation for short visits For short visits, we recommend searching for accommodation on Airbnb. In addition, a limited number of apartments are available in the Alexander von Humboldt Guest House .

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University of Bayreuth

International, innovative, and interdisciplinary in research and teaching

Top-notch research, state-of-the-art teaching methods, international influences, diversity, and a springboard to a successful career – these are all things the University of Bayreuth stands for.

The University of Bayreuth is a dynamic campus university with currently about 13,000 students. Beyond the interdisciplinary research focus and excellence in teaching, the university has a clear vision of social responsibility and entrepreneurship. In the middle of Kulmbach , our new satellite campus unites the perspectives of natural science, economics, law, social sciences, and behavioural science in one place in a way that has not yet been seen in Germany.

We have a close network of strategically selected, international research partners, and we have strategic partnerships with universities around the globe. A wide range of innovative BA, MA and PhD programmes as well as our international summer schools are conducted in English . There are presently around 1,770 international students from more than 100 countries on the Bayreuth and Kulmbach campuses. Focus areas in research include Nonlinear Dynamics, Polymer and Colloid Science, Molecular Biosciences, Ecology and Environmental Sciences, New Materials, African Studies, High Pressure and High Temperature Research, Cultural Encounters and Transcultural Processes, Innovation and Consumer Protection, Food and Health Sciences, Energy Research and Energy Technology, Governance and Responsibility.

Our university has an outstanding staff-to-student ratio. Our high performance levels, multidisciplinary collaborations and scientific excellence result in high-ranking positions . In the 2021 THE ranking of “475 under 50”, the University of Bayreuth once again achieved a top position among the best universities in Germany. We have proven expertise in campus and curriculum internationalisation , which is confirmed by the results of the extended internationalisation audit conducted by the German Rectors’ Conference, a close and successful project cooperation with the German Academic Exchange Service (DAAD) and a number of Alexander von Humboldt Awards for our international management and service .

Welcome to our one-of-a-kind campus in Bavaria! It is both the heart of our university and a source of inspiration. It is where friendships are made, collaboration is initiated, and ideas are conceived, ensuring that our university remains a beacon of innovation. Scientific exchange profits tremendously from the wide variety of disciplines our communicative campus brings together.

Coronavirus Be sure that there is a comprehensive and high-quality range of courses in all degree programmes that will enable students to successfully begin and continue their studies – on campus or online.

University location

Welcome to Bavaria! Seventy-six thousand people from 145 nations chose the city of Bayreuth as their new home, and those numbers are steadily rising. Because of its 13,000 students, Bayreuth is the third -youngest city in Germany . Living here means not getting stuck in traffic jams every morning. The short distances in Bayreuth allow you to leave your car at home and to walk or ride your bike to work and to campus.

Career & Networking The Welcome Service at the University of Bayreuth is here to assist you with any questions regarding living and working in the region of Bayreuth. The team of our International Office provides guidance and support for all international students, scholars, and their families before, during and after their stay at the University of Bayreuth. The aim is to ensure a quick, trouble-free and thus successful start as well as an unforgettable stay on our friendly campus in Bayreuth and Kulmbach.

A good climate for your entrepreneurial spirit The University of Bayreuth takes its role as a regional motor of innovation very seriously. The Office of Entrepreneurship & Innovation was set up to help students gain the necessary qualifications in the area of entrepreneurial and innovative thinking and acting. In doing so, we nurture our students’ willingness and ability to start their own companies. Our Adviser for Start-Ups provides start-ups and prospective start-ups with confidential guidance and support. Strong innovation and research institutions combined with high-performance medium-sized companies make for a steady rise in workforce and a guarantee for job security.

Your new home away from home Bayreuth and Kulmbach offer a fast variety of childcare services as well as affordable living space . Step outside your front door and enjoy the recreational activities . Scenic northern Bavaria is great for sports activities: cycling, hiking, mountain-climbing, canoeing, rafting, and skiing are all excellent ways to enrich your leisure time. However, if you do happen to crave the feel of a bigger city now and then, Nuremberg is only a one-hour train ride away and Munich is only two-and-a-half hours away. You can reach Berlin in four hours by car or bus and Leipzig in half that time.

Have a great start. We look forward to meeting you!

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Welcome to the Homepage of the research group Numerical Analysis and Uncertainty Quantification at University of Heidelberg

Postal address

Institute of Applied Mathematics and Interdisciplinary Center for Scientific Computing (IWR) Universität Heidelberg Im Neuenheimer Feld 205 69120 Heidelberg, Germany

Herta Fitzer NumOpt [at] uni-heidelberg.de +49 6221 5414111 Room: 1 / 318

Many physical models from the natural sciences and from engineering involve sources of uncertainty that affect their outputs. An example are variations in the orientation of layers of carbon fibres occurring naturally in the manufacturing process of aircraft wings. As a consequence, the locations and types of possible defects and cracks are difficult to predict precisely. The goal of uncertainty quantification is to use mathematical and computational methods to account for such uncertainties, and to understand how they propagate through to model outputs.

The research of our group focuses on developing innovative numerical methods to efficiently quantify uncertainty. We apply these techniques to tackle data-driven, large-scale problems that are typically modelled in the form of differential equations. Our methods strive for a balance between efficiency, a rigorous mathematical foundation and realistic model problems. As a brief overview, some of the techniques we develop are based on Monte Carlo/quasi-Monte Carlo sampling, stochastic collocation, sophisticated hierarchical/multilevel strategies and dimension reduction through low-rank tensor approximations. The applications we study range from groundwater flow, to nuclear physics and carbon fibre composites in manufacturing.

When studying real world processes mathematically, essentially all problems can be roughly put into two classes: Forward and inverse problems. A forward problem concerns the calculation of the state of a physical system, given all the necessary parameters, as well as boundary and initial conditions. Inverse problems, on the other hand, are concerned with computing parameters given observations of the state of the system. Good estimates of these parameters give us an insight into hidden quantities that typically cannot be observed directly and are otherwise very difficult to grasp. Consequently, inverse problems are among the most important in mathematical applications and uncertainty quantification plays a crucial role here.

A very popular approach to address inverse problems is Bayesian inference, a subbranch of statistics and data science. It facilitates the quantification of uncertainties regarding the model and its parameters. This resolves the inherent ill-posedness of inverse problems and has proven to be exceedingly fruitful in many applications. A particular focus in our group is the design and analysis of efficient numerical techniques for high- and infinite-dimensional Bayesian inverse problems, especially those constrained by differential equations. We look at the effect of choosing high-level priors, develop efficient multilevel algorithms and surrogates, incorporate novel ideas from numerical analysis into this setting and explore links to machine learning.

Natural or engineered materials often contain two or more key constituents, arranged in a heterogeneous structure varying at different scales. Such materials are desirable because their macroscopic properties can be superior to the properties of the individual constituents. It is even possible to explicitly design them for a particular purpose by changing the composition of the constituents. An example are carbon fibre composites for lightweight structures and vehicles. The mathematical modelling of such heterogeneous or composite materials naturally leads to partial differential equations (PDEs) with highly oscillating coefficients. Direct numerical solution of such problems with traditional methods, such as finite elements is computationally expensive. Just to compute the correct qualitative behaviour, the mesh resolution would need to be sufficiently high to capture all the fine scale variation.

In our group, we study and develop multiscale numerical methods that do not suffer from this drawback. We are particularly interested in systems without periodic structure or scale separation and in problems with a high contrast in the constituent material properties. Such problems require customized approximation spaces, computable via localised boundary value or eigen-problems. There are also strong links to model order reduction and domain decomposition methods. Examples include multiscale finite elements, generalised multiscale finite elements, or the localizable orthogonal decomposition method. Target applications are again subsurface flow and carbon fibre composites, but also biological materials such as bone or cells.

Scientific computing and numerical simulation are playing an ever more important role in science and technology. Hardly any new developments, e.g., in engineering or the geosciences, take place without careful mathematical modelling, analysis and optimisation. More and more complicated systems are being tackled, in particular in the life sciences or in the context of climate change. This requires continued research into efficient and robust numerical methods, especially in the context of heterogeneous or random media, and in their careful and rigorous numerical analysis. Increased efficiency requires a redesign of traditional algorithms to harness the power of modern many-core computing architectures (hardware-aware scientific computing), while data-driven (predictive) scientific computing poses new challenges for the robustness of existing methods.

Here, the focus of our group encompasses

• traditional topics, such as efficient preconditioning and discretisation methods for heterogeneous and anisotropic PDEs, or efficient algorithms for large-scale eigenproblems, as well as
• more modern topics that arise naturally in the context of uncertainty quantification or Bayesian inference, such as high-dimensional approximation and quadrature, including Monte Carlo, quasi-Monte Carlo, sparse grid, low-rank tensor approximation or deep learning.

In terms of novel software, we contribute in particular to DUNE (the Distributed and Unified Numerical Environment) and to MUQ (the MIT Uncertainty Quantification Library).

shape digital change

Become part of the megatrend, high performance computing / quantum computing, m.sc., faculty of applied computer science.

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Course content.

boost your understanding of the universe

Course fact sheet.

Degree: Master of Science (M.Sc.)

Duration : 3 semesters (1.5 years)

ECTS points: 90

Start: March (summer semester)

Location : Deggendorf

Taught in:  English

Application period: 15 November - 15 January

Please note that - regardless of the opening of the applicant portal - applications for the High Performance Computing / Quantum Computing master degree programme can only be considered if they have been submitted by 30 November . This internal deadline, which must be observed urgently, is due to organisational reasons - applications received later, will likely not be accepted, as the underlying administrative process can no longer be completed in time.

Admission requirements:

• Bachelor degree in Computer Science, Physics, Technology or a related subject
• A successfully completed assessment test. Further information: Assessment test
• The online admission test will take place on 6 December 2023, 1 pm – 4 pm.
• If you decide to take the admission test “on site” in Deggendorf, the test date is 8 December, 9 - 10.30 pm.
• English level B2
• If English is not your native language, proof of sufficient English skills is necessary

Application procedure: Read step by step of how to apply

• €72 student union fee per semester
• * International applicants and students

Download :  Course flyer

• Any queries about this specific study programme:  [email protected]
• For general info about studying at DIT contact our prospective student advisors or send an e-mail to  [email protected]
• Information for freshers
• Advice and support
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Career prospects.

As an expert in high-performance computing and quantum computing, you are in demand wherever large data centres are set up or used. You will find these more and more frequently in Germany, but the international market also offers jobs for you. Since the programme at DIT teaches you the general basics of data centre management, you can be employed as an expert in both academic and private data centres (medium-sized companies and upwards). The focus on high-performance computing and quantum computing is currently being massively expanded with corresponding specialist personnel. Larger companies that perform complex computations themselves or provide them as service providers for others can also make good use of graduates like you. Your tasks range from designing a system together with the customer, to planning and setting it up, to programming. You can also take over the maintenance of systems, i.e. you make sure that the system as a whole runs fail-safe and intrusion-proof. In any case, you can be there to see how technology develops even before it changes our everyday lives in the future.

In this profession, it is quite advantageous to have at least an idea of neighbouring areas, even if you may have specialised in a slightly different field in everyday life. You bring along mathematically sound and logical thinking as your personal qualities. You also have integrits and a willingness to communicate in your everyday work. 

You can build a career in one of the following professional fields:

• IT and IT infrastructure
• (IT) security and safety
• Hardware and software design
• Operating systems and system design
• Programming
• Planning and construction of data centres
• Power supply/USV
• Fire protection
• Building services and HVAC
• IT management
• Innovation management

subject overview

Overview of  lectures and courses , SWS (Semesterwochenstunden = weekly hours/semester) and  ECTS (European Credit Transfer and Accumulation System) in the Master's degree High Performance Computing / Quantum Computing.

Qualification Goals

Module handbook, study and exam regulations, lecture schedules, exam schedules, documents & organisation.

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Department of Computer Science

Tum school of computation, information and technology.

• Technical University of Munich

From software and data science to supercomputing and Artificial Intelligence, the Department of Computer Science in the School of Computation, Information and Technology at the Technical University of Munich conducts research on and teaches in Informatics and in interdisciplinary fields of research. We train our students in the fundamentals of Computer Science and its applications. Our work focuses on practical application and our research is carried out in close cooperation with industry partners.

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Study with us.

PhD Position in Scientific Computing (f/m/d) - Cologne, Germany

The University of Cologne is one of the largest and most research-intensive universities in Germany, offering a wide range of subjects. With its six faculties and its interfaculty centres, it offers a broad spectrum of scientific disciplines and internationally outstanding profile areas, supported by the administration with its services.

Prof. Gregor Gassner (www.mi.uni-koeln.de/NumSim/) invites applications for a PhD position in scientific computing.

Research and teaching in an international team

Construction, development and analysis of numerical methods (Discontinuous Galerkin methods) for hyperbolic PDEs in plasma physics

Extension and development of the open source simulation code Trixi.jl (written in Julia Language)

Participation at international workshops and conferences

Working on project B1 within the DFG funded research unit SNUBIC (for more details on project B1 visit www.snubic.io)

YOUR PROFILE

Excellent master degree in applied math or related fields

Experience with Discontinuous Galerkin or Finite Volume methods is desirable

Experience in development and design of numerical simulation software

Programming languages, e.g., Julia, Fortran, C/C++ or Python

Knowledge in the area of numerical methods for hyperbolic PDEs (e.g. Discontinuous Galerkin method and/or Finite Volume methods)

Willingness to work on projects and in teams

WE OFFER YOU

a diverse and fair working environment

support in reconciling work and family life

flexible working time models

extensive advanced training opportunities

occupational health management offers

local transport ticket at a discount for UoC employees

opportunity for remote work

The position is available from 01.10.2022 on a part-time basis with 29,87 hours per week. The position is to be filled for a fixed term until 30.09.2026. If the necessary prerequisites required by tariff regulations as well as the sought after personal qualifications are met, the salary will correspond to the pay grade 13 as specified in the States’ Tariff Agreement (TVL).

The University of Cologne is committed to equal opportunities and diversity. Women are especially encouraged to apply and will be considered preferentially in accordance with the Equal Opportunities Act of North Rhine-Westphalia (Landesgleichstellungsgesetz – LGG NRW). We also expressly welcome applications from people with disabilities / special needs or of equal status.

Please apply online at: https://jobportal.uni-koeln.de with proof of the sought qualifications. The reference number is Wiss2205-17. The application deadline is10.07.2022.

If you have any questions, please contact Prof.Dr. Gregor Gassner ([email protected]).

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Ph.D. in Scientific Computing

This program is intended for University of Michigan Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. A firm knowledge of the scientific discipline is essential.

This is not a stand-alone degree; it is a joint degree program . Students must be accepted into the Ph.D. program of a home department at the University of Michigan. The actual degree name will have “…and Scientific Computing” appended to the the normal title, e.g., “Ph.D. Degree in Aerospace Engineering and Scientific Computing.”

Students in the Scientific Computing degree program come from many different disciplines. Our current enrollment exemplifies the breadth of departments, schools, and colleges represented by our Ph.D. students.

Students may enroll in the program after having completed one term in their home Ph.D. department. We recommend applying prior to being promoted to candidacy status, but can often accommodate students later in their degree progress.

Please contact MICDE at [email protected] if you have any questions about the Ph.D. in Scientific Computing.

Academic Requirements

Application procedures.

Current Students

Tracking Progress

Funding Resources

Current Enrollment

Students must complete the normal doctoral requirements of their home departments, as well as additional requirements in scientific computing. The specific requirements are:

Non-exhaustive examples of course selections for various departments can be seen on our  Example Course Choices page.

Group I Courses

Twenty-four (24) credit hours of coursework toward your home degree. You must complete your home degree requirements in order to receive the Ph.D. in Scientific Computing. It cannot be earned on its own. Group I may overlap with groups II or III. 

Group II Courses

Nine (9) credit hours of approved courses in scientific computing methodologies.

Group III Courses

Nine (9) credit hours of approved courses in computational science and applications in scientific computing outside the home department  (this typically includes courses in computer science, parallel algorithms, advanced computer architectures, computational fluid dynamics, or other courses in scientific computation not offered by a student’s home department).

Committee Composition

An emphasis on scientific computing reflected in doctoral thesis and doctoral committee composition. At least one faculty member on your committee should be an expert in scientific computing, affiliated with MICDE  or  MIDAS .

Demonstration of Understanding

Preliminary/Qualifying Exam Question: You must answer at least one question related to scientific computing during your department’s preliminary or qualifying examination.  If you join the program after having completed your qual/prelim, you can still use this option if you were asked a question related to computational methods or applications during your qual/prelim.  The student’s advisor or a MICDE  or  MIDAS -affiliated member of the committee must then email MICDE to confirm that this requirement is complete.

If the format of your PhD program’s preliminary/qualifying examination cannot accommodate this requirement, or if you are beyond this stage at the point of joining the program and were not asked a question on your prelim/qual, you have the following option to complete the Demonstration of Understanding requirement:

Literature Review: A 3-5 page critical assessment of previous research that has been done in your research area, specifically the scientific computing/computational aspect of your research problem.  This must be submitted to [email protected]  for review 2-4 semesters before your dissertation defense.

If you have any questions about fulfilling the Demonstration of Understanding requirement, please email [email protected] .

For Faculty:

Please send an email to [email protected] describing the scientific computing-related question that was asked during the examination and acknowledging that the student answered the question satisfactorily.

Ph.D. Seminar

If you enrolled in the Ph.D. in Scientific Computing in or after January 2022 , you are required to present at least once before graduation in the Ph.D. Student Seminar Series . Before presenting, you are strongly encouraged to attend as many sessions of the the Ph.D. Seminar Series as you can, from students in your department and outside it. The Ph.D. Student Seminar Series is an opportunity to learn how to simplify your explanation of your research problems and methods in order to talk about them to colleagues outside of your lab or your home department, which will help you prepare for future job searches.

Sign up to present in 2023-2024 on the MICDE Ph.D. Student Seminar Sign-Up form .

Students are expected to work closely with their academic advisors and with MICDE to develop a plan to meet these requirements.

• Talk to your academic advisor about your interest in the Ph.D. in Scientific Computing. Your department must approve your enrollment in the program.
• Submit the Course Audit form . You don’t have to have a full plan in place before filling out the Course Audit form, but please spend some time considering each of the questions and put your answers in the formats requested.
• After the MICDE program administrator checks your Audit Form and transcript, they will contact you to schedule an advising session with an MICDE Management & Education Committee faculty member. During the session, you, the faculty member, and the MICDE program administrator will finalize your plan to meet the requirements of the Ph.D. in Scientific Computing.
• After your advising session, you can apply to the Ph.D. in Scientific Computing . In order to apply, you must complete the Rackham Application Form , have it signed by your department, and submit it to [email protected] . You are not enrolled in the program unless you have completed this step.

Questions? Contact the Program Administrator at [email protected] .

Eligibility

This is not a stand-alone degree; it is a   joint degree program . Students must be accepted into the Ph.D. program of a home department at the University of Michigan-Ann Arbor.

Enrollment Deadlines

Students are enrolled on a rolling basis as they apply.

Information for Current Students

Please contact the program administrator ( [email protected] ) for all questions related to the Ph.D. in Scientific Computing.

We track students’ progress through the Ph.D. in Scientific Computing Web Progress Form . Your Web Progress Form is created after the advising session, and is accessible by prospective students as well as those who are enrolled. Every summer we will reach out to students to update their Web Progress Form with anything that has changed since the previous summer.

Updating the Web Progress Form

Please plan to update your Web Progress Form each summer with new information, including:

• If you answered questions about scientific computing in your quals/prelims and your Web Progress Form does not reflect this, please describe the questions in the Candidacy Status section.
• If you have formed your doctoral committee, please list the members in the Committee Information section.
• If you have made any changes to the courses you took or plan to take to fulfill requirements for the Ph.D. in Scientific Computing (including changing courses from “planned” to “completed” once you’ve taken them) please update the Course requirements section.
• If you have made progress in your research that is not yet reflected on your WPF (awards, fellowships, conference presentations, publications, etc.) please update the Research Progress section.
• Please make sure that your current estimated graduation term is listed in the  Future Plans section. This is not set in stone, but helps us to understand where you are in your degree process.

Enrollment Status

Note that each student has one of the following 5 statuses on the Web Progress Form . If you believe the enrollment status listed on your Web Progress Form is incorrect, please email [email protected] .

• Enrolled  ( had an advising session, turned in their application form to MICDE and Rackham has processed the application )
• Prospective  ( had an advising session, but has not yet enrolled ) Please let us know if you are still interested in enrolling in the program so we can finish your enrollment. You can log in to the Web Progress Form to see what courses were discussed in your original advising appointment.
• Leave of Absence  ( you are enrolled in the program, and currently in a leave of absence from your home program ) Please let us know when you return from a leave of absence.
• Graduated  ( you graduated from the program in 2015 or later)
• Discontinued  (you discontinued the Ph.D. in Scientific Computing and/or your home program)

You can view your Web Progress Form at any time. If you want to make any changes to your Web Progress Form outside of the summer window, or if you have any problems with accessing the form, please email [email protected] .

• Confirm that your transcript shows you are enrolled in the PhD in Scientific Computing.  If your transcript doesn’t show your enrollment in the program, please contact the program administrator ( [email protected] ) to find out your status within the program.
• If your transcript shows your enrollment in the Scientific Computing program, please review all the information we have on file for you on the Web Progress Form . In particular, check the Graduation requirements summary section at the top. If any of the boxes are blank or incomplete, please ask the program administrator ( [email protected] ) to review your requirements and confirm that they are complete.
• During the term you want to graduate, please contact the program administrator ( [email protected] ) to let them know so they can process your information.

Don’t forget to add the PhD in Scientific Computing program to the title page of your dissertation! For example:  (Physics and Scientific Computing)

A1: Please see  this list for examples. Note that they are only samples of what other students have done, but they are not the only choices. This degree is extremely individualized, so please email the program administrator ( [email protected] ) for more course information.

Q2: I met with the program director, but I get an error when I try to access the Web Progress Form. What can I do?

A2: Please contact the program administrator ( [email protected] ) to inquire about your status.

Q3: Can I change the courses listed on my form?

A3: Yes, but note that any course changes must be approved by MICDE. Email the program administrator ( [email protected] ) if you have any questions.

Q4: How often are students required to complete the Web Progress Form

A4: We ask students to fill out the form annually, by the end of summer each year.

Q5: What if I want to know if a course is approved before the Annual Form is due?

A5: Please contact the program administrator ( [email protected] ) to initiate the approval process. Once approved, they will record it in your form.

Q6: The form lists my status as “PROSPECTIVE” but I think I should be enrolled. What should I do?

This bar graph represents the numbers of students from different departments at U-M enrolled in the program. Students come from the College of Engineering, School of Kinesiology, College of LSA, Michigan Medicine, College of Pharmacy, Ross Business School, School for Environment and Sustainability, School of Information and the School of Public Health.

Departments include: Aerospace Engineering, Civil and Environmental Engineering, Biomedical Engineering, Chemical Engineering, Climate and Space Sciences and Engineering, Electrical Engineering and Computer Science, Industrial & Operations Engineering, Macromolecular Science & Engineering, Mechanical Engineering, Materials Science & Engineering, Naval Architecture & Marine Engineering, Nuclear Engineering & Radiological Sciences, Applied Physics, Chemistry, Chemical Biology, Earth and Environmental Sciences, Linguistics, Mathematics, Physics, Political Science, Psychology, Biostatistics, Environmental Health Sciences, Epidemiology, Health Behavior & Health Education, Kinesiology, Health Infrastructures & Learning Systems, Neuroscience, Pharmaceutical Sciences, Business and School for Environment and Sustainability.

This list is not exhaustive, and continues to grow.

Ph.D. in Scientific Computing years in existence

Current Ph.D. in Scientific Computing students

Alumni since 1992

History of the Ph.D. in Scientific Computing

Text Version

Faculty Leadership

For all questions about the Ph.D. in Scientific Computing, please email [email protected] .

Karthik Duraisamy

2022 – present

2004 – 2022

Bill Martin

1988 – 2004

Quantum Information Portal and Wiki

Phd in quantum computing at covestro leverkusen/cologne germany, job type: , tags: .

• #quantum computing #quantum information #chemistry

Application deadline: 

Employer web page: .

We offer a fully funded three year PhD position in quantum computing for computational chemistry in an extraordinary environment.

During your doctorate, you will work in our quantum computing team (lead by Dr. Christian Gogolin) and computational quantum chemistry in the Digital Research & Development department of Covestro , while benefiting from the scientific environment at the University of Cologne , which will also grant the degree. The PhD will contribute to a BMBF (Bundesministerium für Bildung und Forschung – German ministry for education and research) funded research project in partnership with experimental quantum computing groups at the Universities of Mainz and Heidelberg . In addition there will be the opportunity to contribute to a multi-year collaboration between Covestro and Google AI Quantum . With this setup, you will get the opportunity to work on a cutting edge technology and do basic science and research, but will also acquire industry experience.

In your three years here you will be a part of the diverse, international, and interdisciplinary Digital R&D team at Covestro and will perform the following tasks:

- You will learn about quantum computing and work on quantum algorithm development and quantum programming through hands on experience - You will learn and apply classical computational chemistry methods - You will collaborate with people towards the goal of pushing boundaries in quantum computing research - Moreover, you will contribute to estimating and realizing the potential of quantum computing for research and development in the chemical industry - As an additional benefit you will acquire scientific programming, software development, and high performance computing skills - Finally, you will communicate the results of your work to the team and publish them in scientific journals for the wider research community

This position is designed for three years, with a flexible starting date. Review of applications will start immediately.

WHAT YOU OFFER

- You are a curious person and strongly interested in doing research - You hold a Master’s degree in Computer Science, Physics, Chemistry, Mathematics, Data Science or similar, or have comparable relevant prior experience in these areas - You have experience with software development in Python, and have already experimented with one of the quantum computing libraries - You have experience with and interest in learning software development workflows and tools - To complete your profile, your command of English is excellent

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Researching extreme environments

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A quick scan of Emma Bullock’s CV reads like those of many other MIT graduate students: She has served as a teaching assistant, written several papers, garnered grants from prestigious organizations, and acquired extensive lab and programming skills. But one skill sets her apart: “fieldwork experience and survival training for Arctic research.”

That’s because Bullock, a doctoral student in chemical oceanography at the Woods Hole Oceanographic Institution (WHOI), spends significant time collecting samples in the Arctic Circle for her research. Working in such an extreme environment requires comprehensive training in everything from Arctic gear usage and driving on unpaved roads to handling wildlife encounters — like the curious polar bear that got into her team’s research equipment.

To date, she has ventured to Prudhoe Bay, Alaska, five times, where she typically spends long days — from 5:00 a.m. to 11 p.m. — collecting and processing samples from Simpson Lagoon. Her work focuses on Arctic environmental changes, particularly the effects of permafrost thaw on mercury levels in groundwater.

“Even though I am doing foundational science, I can link it directly to communities in that region that are going to be impacted by the changes that we are seeing,” she says. “As the mercury escapes from the permafrost, it has the potential to impact not just Arctic communities but also anyone who eats fish in the entire world.”

Weathering a storm of setbacks

Growing up in rural Vermont, Bullock spent a lot of time outside, and she attributes her strong interest in environmental studies to her love of nature as a child. Despite her conviction about a career path involving the environment, her path to the Institute has not been easy. In fact, Bullock weathered several challenges and setbacks on the road to MIT.

As an undergraduate at Haverford College, Bullock quickly recognized that she did not have the same advantages as other students. She realized that her biggest challenge in pursuing an academic career was her socioeconomic background. She says, “In Vermont, the cost of living is a bit lower than a lot of other areas. So, I didn’t quite realize until I got to undergrad that I was not as middle-class as I thought.” Bullock had learned financial prudence from her parents, which informed many of the decisions she made as a student. She says, “I didn’t have a phone in undergrad because it was a choice between getting a good laptop that I could do research on or a phone. And so I went with the laptop.”

Bullock majored in chemistry because Haverford did not offer an environmental science major. To gain experience in environmental research, she joined the lab of Helen White, focusing on the use of silicone bands as passive samplers of volatile organic compounds in honeybee hives. A pivotal moment occurred when Bullock identified errors in a collaborative project. She says, “[Dr. White and I] brought the information about flawed statistical tests to the collaborators, who were all men. They were not happy with that. They made comments that they did not like being told how to do chemistry by women.”

White sat Bullock down and explained the pervasiveness of sexism in this field. “She said, ‘You have to remember that it is not you. You are a good scientist. You are capable,’” Bullock recalls. That experience strengthened her resolve to become an environmental scientist. “The way that Dr. Helen White approached dealing with this problem made me want to stick in the STEM field, and in the environmental and geochemistry fields specifically. It made me realize that we need more women in these fields,” she says.

As she reached the end of college, Bullock knew that she wanted to continue her educational journey in environmental science. “Environmental science impacts the world around us in such visible ways, especially now with climate change,” she says. She submitted applications to many graduate programs, including to MIT, which was White’s alma mater, but was rejected by all of them.

Undeterred, Bullock decided to get more research experience. She took a position as a lab technician at the Max Planck Institute of Marine Microbiology in Bremen, Germany, where she studied methane emissions from seagrass beds — her first foray into chemical oceanography. A year later, she applied to graduate schools again and was accepted by nearly all of the programs, including MIT. She hopes her experience can serve as a lesson for future applicants. “Just because you get rejected the first time does not mean that you’re not a good candidate. It just means that you may not have the right experience or that you didn’t understand the application process correctly,” she says.

Understanding the ocean through the lens of chemistry

Ultimately, Bullock chose MIT because she was most interested in the specific scientific projects within the program and liked the sense of community. “It is a very unique program because we have the opportunity to take classes at MIT and access to the resources that MIT has, but we also perform research at Woods Hole,” she says. Some people warned her about the cutthroat nature of the Institute, but Bullock has found the exact opposite to be a true. “A lot of people think of MIT, and they think it is one of those top tier schools, so it must be competitive. My experience in this program is that it is very collaborative because our research is so individual and unique that you really can’t be competitive. What you are doing is so different from any other student,” she says.

Bullock joined the group of Matthew Charette, senior scientist and director of the WHOI Sea Grant Program , which investigates the ocean through a chemical lens by characterizing the Arctic groundwater sampled during field campaigns in Prudhoe Bay, Alaska. Bullock analyzes mercury and biotoxic methylmercury levels impacted by permafrost thaw, which is already affecting the health of Arctic communities. For comparison, Bullock points to mercury-based dental fillings, which have been the subject of scientific scrutiny for health impacts. She says, “You get more mercury by eating sushi and tuna and salmon than you would by having a mercury-based dental filling.”

Promoting environmental advocacy

Bullock has been recognized as an Arctic PASSION Ambassador for her work in the historically underresearched Arctic region. As part of this program, she was invited to participate in a “sharing circle,” which connected early-career scientists with Indigenous community members, and then empowered them to pass what they learned about the importance of Arctic research onto their communities. This experience has been the highlight of her PhD journey so far. She says, “It was small enough, and the people there were invested enough in the issues that we got to have very interesting, dynamic conversations, which doesn’t always happen at typical conferences.”

Bullock has also spearheaded her own form of environmental activism via a project called en-justice , which she launched in September 2023. Through a website and a traveling art exhibit, the project showcases portraits and interviews of lesser-known environmental advocates that “have arguably done more for the environment but are not as famous” as household names like Greta Thunberg and Leonardo DiCaprio.

“They are doing things like going to town halls, arguing with politicians, getting petitions signed … the very nitty-gritty type work. I wanted to create a platform that highlighted some of these people from around the country but also inspired people in their own communities to try and make a change,” she says. Bullock has also written an op-ed for the WHOI magazine, Oceanus , and has served as a staff writer for the MIT-WHOI Joint Program newsletter, “ Through the Porthole .”

After she graduates this year, Bullock plans to continue her focus on the Arctic. She says, “I find Arctic research very interesting, and there are so many unanswered research questions.” She also aspires to foster further interactions like the sharing circle.

“Trying to find a way where I can help facilitate Arctic communities and researchers in terms of finding each other and finding common interests would be a dream role. But I don’t know if that job exists,” Bullock says. Given her track record of overcoming obstacles, odds are, she will turn these aspirations into reality.

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Advancing technology for aquaculture

Using deep learning to image the Earth’s planetary boundary layer

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