(For the curriculum valid until winter semester 2011/1012 see here)

The curriculum combines different flavors of Computational Science to a single degree program, which correspondingly aims at graduates of quite different disciplines. In addition, the program allows students to cross disciplinary boundaries. As a result, only few courses of the curriculum are mandatory and students have lots of options to choose from.

The courses offered can be divided into three groups: (i) the generic education in scientific computing (including applied math), (ii) the training in high performance computing, and (iii) the preparation for computer-based research in the natural sciences (including neuroscience). The basic structure of the curriculum is shown in the following table:

Sem. | Structure of Curriculum | CP total | ||||

core curriculum (mandatory subjects) | required electives in computer science and mathematics | specialization |
||||

1 | Modeling and Simulation 1 (8 CP) | High Performance Computing (6 CP) | to be chosen from list of available subjects (8 CP) | subjects from chosen field of specialization (6-10 CP) | 28-32 | |

2 | Modeling and Simulation 2 (8 CP) | Computer Lab High Performance Computing (6 CP) | to be chosen from list of available subjects (4 CP) | subjects from chosen field of specialization (10-14 CP) | 28-32 | |

3 | Specialization 1 (15 CP, mandatory) | 30 | ||||

Specialization 2 (15 CP, mandatory) | ||||||

4 | Master Thesis (30 CP, mandatory) | 30 |

During the first year the curriculum consists of three components, which are taught in parallel: (i) a mandatory core curriculum, covering those techniques of scientific computing which are utilized in many areas of computational science, (ii) a field of specialization at the students' choice, in which students either gain in-depth knowledge in scientific or high performance computing or acquire expertise in the field in which they later want to apply scientific computing, (iii) a number of math and computer science subjects, in which students receive additional methodological training for their field of specialization, primarily in numerical math.

The core curriculum consists of the subjects "Modeling and Simulation" and "High Performance Computing". The two courses in "Modeling and Simulating" provide the core elements of scientific computing. They are based on the strong background in math which our students have acquired during their undergraduate studies. (including the basics of numerical math). In the courses education in advanced numerical math is inherently intertwined with aspects of modeling. This mathematically oriented subject is complemented by a course in "High Performance Computing", in which the students become familiar with modern computer architectures, parallel computing, usage of GPUs etc. Practical experience with high performance computing is gained during the computer lab class in the second semester: the exercises cover all facets of parallel computing, including access to one of the most powerful parallel computers in Germany. The courses of the core curriculum are all mandatory, in accordance with their interdisciplinary nature.

The available fields of specialization are:

- Scientific Computing
- Algorithms for Large Data Sets
- Computer Engineering
- Computational Math Finance
- Neuroscience
- Meteorology and Climate Research
- Geophysics and Crystallography
- Lattice Gauge Theory
- Solid State Physics

This part of the curriculum allows students to focus on quite different aspects of computational science, with the various fields aiming at Bachelor graduates of different disciplines: An in-depth training in numerical methods and algorithms is offered by the specialization "Scientific Computing", but also by "Algorithms for Large Data Sets". The specialization "Computer Engineering" is designed for students who want to become experts in high performance computing. The remaining specializations prepare for the application of computational methods in math finance and the natural sciences. In "Computational Math Finance" the students primarily learn how to solve stochastic differential equations and how to apply them to math finance problems. The specializations in the natural sciences can be characterized as "Computational Neuroscience", "Computational Meteorology" and "Computational (Geo)Physics". On the one hand, this part of the curriculum contains courses from the corresponding Bachelor's and Master's programs, in which, prior to any numerical implementation, the basic concepts (fundamental equations, approximations and models) of the fields are introduced. This background material is then, mostly during the second semester, complemented by courses in which the computational methods specific to the individual disciplines are introduced. This includes in particular the derivation of the models that underlie actual computer simulations as well as the preparation of relevant equations for numerical treatment (as, for instance, the discussion of boundary conditions and symmetries, the estimation of the numerical effort etc).

The third component of the first year curriculum, the module "Computer Science and Mathematics", also offers students several options: on the one hand, the courses of this module are intended to ensure a broad mathematical background of our graduates, especially in numerical math. On the other hand, the module allows students to prepare themselves for mathematically advanced Master's projects, for instance in the fields "Computational Math Finance" or "Lattice Gauge Theory". Moreover, by attending additional lab classes in "Modeling and Simulating" students get ready for a Master's project in "Scientific Computing". Accordingly, the module consists of a wide range of required elective subjects. Suggestions for the course of study in the specific fields of specialization can be found here.

The second year of study is completely dedicated to the Master's project: in order to account for the particular demands of research in math and the natural sciences, the actual work on the project is preceded by a preparatory period (modules Specialization 1,2). In this period the students are introduced to both general and subject-specific techniques of research. In particular, the students are introduced to the concepts of scientific publication and the specific literature required for the intended research project. As a second crucial concept of scientific practice, the students learn to write an application for a scientific project: on the basis of a brief description of the scientific background the question to be investigated in the Master's thesis as well as the methods to be used are characterized in detail in this project proposal. In addition, the students familiarize themselves with the analytical techniques and numerical codes required for their Master's project. In this way students are systematically led to their Master's projects, allowing them to address questions at the forefront of science. The actual actual work on the project is then carried through during the fourth semester.

During the complete second year every student is guided by a faculty member. The project's topic is chosen in agreement with this thesis advisor at the beginning of the third semester.