Doctoral courses in Scientific Computing
Introduction
The doctoral subject of Scientific Computing is based on four mandatory courses that are offered regularly. Typically, two of them are offered each year. In addition to the mandatory courses, other courses and seminars are organized to cover special issues and timely material.
The individual curriculum for a doctoral student should contain both the mandatory courses and additional courses of particular relevance for his/her research project. Finally, it is emphasized that the curriculum should not only contain courses and seminars. Some portion of the curriculum has to be an individual reading of relevant literature.
General
Courses offered in 2024
| course | broad | start | teacher |
|---|---|---|---|
Simulation techniques , 5hp |
yes | Spring, 2024 | Sandra May, Murtazo Nazarov and Ken Mattsson |
Numerical Linear Algebra and Optimization , 7.5hp |
yes | Period 2 autumn 2024 | Roman Iakymchuk, Prashant Singh, and Martin Almquist |
Mandatory courses
| course | abbreviation | credits | description |
|---|---|---|---|
| Mathematical foundations of scientific computing | MF | 7,5 credits | Milestones in functional analysis and approximation theory with relevance to Neural Networks, basics of probability theory. |
| Simulation technologies | ST | 7,5 credits | Deterministic and stochastic techniques to perform model simulations. |
| Numerical linear algebra with applications | NLA | 7,5 credits | Fundamental matrix factorization techniques and their efficient computation; subspace iteration methods and acceleration techniques, (non-)randomized algorithms with emphasis on big data. |
| Techniques and technologies for scientific software engineering | TTSSE | 7,5 credits | Shared memory parallelism, testing practices, reproducible experiments and computational environments, version control and code sharing, maintaining code over time, licensing concerns. |
Tentative time-plan for mandatory courses
| 2023 | 2024 | 2025 | 2026 |
|---|---|---|---|
| MF | ST | MF | ST |
| TTSSE | NLA | TTSSE | NLA |
Other courses
Courses given regularly
| course | broad | last instance | periodicity (years) |
|---|---|---|---|
| Mathematical and numerical techniques for Partial Differential Equations (PDE), 10 hp | yes | 2023 | 3-4 |
| Numerical Functional Analysis (NFA), 5 hp | no | 2022 | 3-4 |
| Numerical Linear Algebra (NLA), 7.5 hp |
yes |
2021 |
3-4 |
| Numerical Methods for ODE (ODE), 7.5 hp |
yes | 2020 |
3-4 |
| Numerical Optimization (NO), 7.5 hp | no | 2021 | 3-4 |
| Parallel Algorithms for Scientific Computing (PASC), 5 hp | yes | 2020 | 3-4 |
| Parallel Programming for Scientific Computing (PPSC), 5 hp | yes | 2022 | 3-4 |
Relevant master level courses
| course |
|---|
| Advanced Numerical Methods, 10 hp |
| Applied Finite Element Methods, 5 hp |
| Large Datasets for Scientific Applications, 5 hp |
| Applied Cloud Computing, 10 hp |
Summer/Winter schools
| school | periodicity | latest | next |
|---|---|---|---|
| Collegio Puteano, Scuola Normale Superiore |
Unknown | 2019 | |
| Hartree |
Unknown | 2016 | |
| Montestigliano |
Annualish | 2014 | |
| Rythms and oscillations |
Unknown | 2014 | |
| Zürich summer school |
Biannual | 2014 | |
| Jyväskylä |
Annual | 2015 | |
| Dobbiaco summer school |
Annual | 2015 | |
| Gene Golub summer school |
Annual | 2015 | Jul 25-Aug 5, 2016 |
| Fluid Dynamics of Sustainability and the Environment |
Annual | 2015 | Sep 5-16, 2016 |
| UPMARC |
Annual | 2015 | |
| High-Order Finite Element and Isogeometric Methods |
Annual | 2014 | |
| Rome-Moscow school of Matrix Methods and Applied Linear Algebra |
Annual | 2014 | |
| International Winter School on Big Data |
2015 | ||
| Oberwolfach seminars |
Few times a year | ||
| Franco-German Summer School on Inverse Problems and Imaging in Bremen |
Sep 18-22, 2017 | ||
| Data Science in Göttingen |
Jul 10-21 2017 |
Other relevant course sites
- UPMARC, Uppsala University
- Centre for Interdisciplinary Mathematics, Uppsala University
- Swedish e-Science Education
- Applied and Computational Mathematics, Royal Institute of Technology
- Department of Mathematics, Uppsala University
Previous courses
Courses given previously on a more regular basis
(SeSE) In collaboration with Swedish e-Science Education
(CIM) In collaboration with Centre for Interdisciplinary Mathematics
Courses offered in 2023
| course | broad | start | teacher |
|---|---|---|---|
| Techniques and Technologies for Scientific Software Engineering (TTSSE), 7.5 hp | yes | Spring, 2023 | Carl Nettelblad, Sven-Erik Ekström |
| Finite Element Method, 7.5 hp | no | August 31, 2023 | Murtazo Nazarov |
| Mathematical and Numerical Techniques for Partial Differential Equations (PDE), 7.5 hp | yes | Autumn 2023 | Sandra May |
| Mathematical foundations for computational science, 7.5 hp | yes | Sep 2023 | Gunilla Kreiss, Elisabeth Larsson, Mohammad Motamed, Stefan Engblom |
Courses offered in 2022
| course | broad | start | teacher |
|---|---|---|---|
| Crash course on mathematics of deep learning, 5 hp | yes | May-June, 2022 | Mohammad Motamed
|
| Parallel programming for scientific computing, 5 hp | yes | February, 2022 | Martin Kronbichler |
| Numerical Functional Analysis (NFA), 5 or 7.5 hp | no | Spring, 2022 | Stefan Engblom |
| Finite Element Method, 7.5 hp | no | August 31, 2022 | Murtazo Nazarov |
Courses offered in 2021
| course | broad | start | teacher |
|---|---|---|---|
| Numerical Linear Algebra (NLA), 7.5 hp |
yes | Spring 2021 | Maya Neytcheva |
| Discontinuous Galerkin (DG) methods for hyperbolic problems, 5 hp | no | March 24, 2021 | Jeniffer Ryan |
| Numerical Optimization, 7.5 hp | no | October 1, 2021 | Ken Mattsson |
| Finite Element Method, 7.5 hp | no | August 31, 2021 | Murtazo Nazarov |
Courses offered in 2020
| course | broad | start | teacher |
|---|---|---|---|
| Finite Element Method, 7.5 hp | no | August 31, 2020 | Murtazo Nazarov |
| Numerical Methods for ODE, 7.5 hp | yes | September 8, 2020 | Gunilla Kreiss |
| SeSE course on Matrices in Statistics with Applications, 5 hp |
yes | September 14-18, 2020 | Maya Neytcheva |
| Parallel Algorithms for Scientific Computing PASC, 5~7.5 hp | no | November 2, 2020 | Maya Neytcheva |
| Numerical Methods in Stochastic Modelling and Simulations, 7.5 hp | yes | January 2020 | Stefan Engblom |
| SeSE course on Machine Learning, 7.5 hp | yes | May 2020 | Salman Toor, Andreas Hellander, Carl Nettelblad |
Courses offered in 2019
| course | broad | start | teacher |
|---|---|---|---|
| Finite Element Method, 7.5 hp | no | September | Murtazo Nazarov |
| Mathematical and numerical techniques for Partial Differential Equations (PDE), 10 hp | yes | February | Gunilla Kreiss |
| Numerical Functional Analysis (NFA), 5 hp | no | April 15 | Stefan Engblom |
| Classical Articles in Numerical Analysis (CA), 7.5 hp | yes | September | Ken Mattsson |
Courses offered in 2018
| course | broad | start | teacher |
|---|---|---|---|
| Uncertainty Quantification, 5 hp or 7.5 hp |
yes | Sep 6 | Mohammad Motamed |
| Numerical linear algebra, 7.5 hp |
yes | Oct 15 | Maya Neytcheva |
| Parallel programming for scientific computing, 5 hp | yes | Dec 3 | Sverker Holmgren |
| Applied Cloud SeSE, 5 hp | yes | Nov 5 | Andreas Hellander |
Courses offered in 2017
| course | start | teacher |
|---|---|---|
| Approximation theory, 7.5 hp |
May 23 | Elisabeth Larsson, Olof Runborg
|
| Numerical optimization, 10 hp |
September 18 | Ken Mattsson, Maya Neytcheva, and Prashant Singh |
| Parallel algorithms for scientific computing, 5 hp | October or November | Sverker Holmgren and Maya Neytcheva |
Courses offered in 2016
| course | start | teacher |
|---|---|---|
| Applied Cloud Computing (SeSE), 5 hp | Period 3 | Andreas Hellander and Salman Toor |
| Numerical Methods in Stochastic Modelling and Simulations (CIM), 7.5 hp | Period 3 | Stefan Engblom and Josef Höök |
| Matrix Computations in Statistics with Applications (SeSE)
|
Period 3 | Maya Neytcheva |
| Numerical methods for ODEs and DAEs, 7.5 hp (recent information and course syllabus) |
Period 1 | Per Lötstedt, Michael Hanke |
| Research projects in Scientific Computing, 7.5 hp | Period 2 | Lina von Sydow |
(SeSE) In collaboration with Swedish e-Science Education
(CIM) In collaboration with Centre for Interdisciplinary Mathematics