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At the beginning of each academic year, each member of staff, in consultation with the mentoring team, draws up an individual annual plan which sets out the research objectives to be achieved and which courses are attended. At the end of each academic year, each member of the staff submits an interim report to his mentoring team, which reports on progress in the doctoral project and in studies. The doctoral candidates always find contact persons for professional and formal problems in the members of their care team.

The study program is divided into three one-year phases:

Introductory phase

Construction phase

Specialization phase

Introductory phase

In this phase, the fellows should take part in courses from the canonical curriculum of applied mathematics. The aim is to provide the broadest possible base of basic knowledge in both stochastics and numerical mathematics.

Functional analysis, optimization, measurement and probability theory and statistical data analysis are offered each year as a 4-hour lecture with exercises, partial differential equations at least every other year as a 4-hour lecture with exercises.

Individual consultation by the support team ensures that after the first year of successful participation in the continuing courses of the college is possible. Depending on the level of knowledge, the teaching program of the first year should include up to 8 semester weekends.

Construction phase

In each semester, at least 4 SWS courses are regularly offered on topics such as statistical inverse problems, applied stochastic processes, image processing, algorithmic and statistical learning, convex optimization or multistage methods. These courses will be offered as 2- or 4-hour lectures, in some cases in the form of compact seminars. The learning program should comprise 4 to 6 semester hours per week during this phase.

Specialization phase

In the final year of the doctoral program, special lectures and seminars on the topic of the doctoral thesis amounting to 4 to 6 semester hours per week should be successfully completed in consultation with the project supervisor. In the interests of a speedy completion of the doctoral studies, the lectures should be closely related to the doctoral project.

Possible courses include topics such as convex optimization in regularization theory, statistical analysis of high-dimensional problems, pattern recognition in biometrics (area A), probabilistic data models, compression methods (area B) or inverse scattering theory and optimization with partial differential equations (area C).

Các hình thức học tập

Toàn thời gian (nhiều lựa chọn)

Học phí
Semester fees: For Winter - €355.31 (includes Administration fees, Student body fees, Railway Ticket fees, Bus Ticket fees, Culture Ticket fees, Studentenwerk fee)
Ngày bắt đầu

Dự kiến Tháng Mười 2021

Địa điểm

University of Gottingen

Wilhelmsplatz 4,


Lower Saxony,

37073, Germany

Yêu cầu đầu vào

Dành cho sinh viên đến từ Mỹ

Student must have Master degree or equivalent in relevant fields.

Dành cho sinh viên quốc tế

A university degree is a general prerequisite to qualify for admission to doctoral studies.

For students from non-English speaking countries, English language proficiency equivalent to level B2 of the Common European Framework of Reference for Languages is required. A level of B1 in German is recommended. TOEFL iBT: 79 – 93 IELTS: 6.5.