Study groups

The PhD topics are devised such that up to eight PhD students (including externally funded PhD students) constitute a study group. Each study group is headed by one of the PIs or by the RTG postdoc. The study groups serve several purposes of supervision, training and social interaction. In an ideal case, the study groups evolve into becoming scientific siblings in the sense that they understand and follow the scientific progress of the different PhD topics. Additionally, these study groups are an instrument for internal discussions and exchange of experiences among the PhD students. They are also the core units for organizing workshops, suggestions on the guest programme, and proposing conference visits.

One of the major task of the study groups is the organization of the winter retreats, which is the central element of communication amongst the PhD students. The winter retreats are three day meetings at remote places without the PIs.

Also, the organization of the summer/autumn schools is done by the study groups. The students did an amazing job in attracting some world leading scientists. These schools were the highlight of the academic years beyond the RTG and attracted external participants from Bremen, Germany and beyond.

In the 1st cohort of the RTG three study groups targeted the core aspects of the three research areas Inverse problems (R1), Direct optimisation (R2) and Data analysis (R3) in particular with a focus on the benchmark applications and three additional study groups on the interfaces between the research areas:

R1-R2: Sparsity and sensitivity, first discretise vs. first optimise
R1-R3: Feature reconstruction and discrepancy with tolerance
R2-R3: Non-linearity decomposition and data based modelling

In the 2nd cohort of the RTG the emphasis of the study groups was on the three research areas Inverse problems, with a focus on PDE based problems and scattering, Optimization, with a focus on direct optimisation with measurement- and process-noise, and Data analysis, with a focus on model learning, regression, classification.

Several students of the 1st cohort continue to be part of the study groups, which leads to larger groups. Moreover the topic of deep learning is useful for several study groups, the weekly seminar of this study group is attended on average by more than 20 students. However, in the following list we only name the primary study group for each student.

Tomographie (R1)

Maximilian Schmidt
Hannes Albers
Johannes Leuschner
Alexander Denker

Optimization and Optimal Control (R2)

Eva Dierkes
Lennart Evers
Ivan Mykhailiuk

Topological Data Analysis (R3)

Lena Ranke
Gideon Klaila

Deep Learning (R1, R2, R3)

Sonal Rami (AWI MarData)
Louisa Kinzel (AWI MarData)
Grace Anulika Eze
Derick Nganyu Tanyu
Helge Mohn (AWI MarDATA)

Deep Learning in Digital Pathology (R3)

Daniel Otero Baguer
Jean Le'Clerc Arrastia
José Carlos Gutiérrez Pérez
Charlotte Janßen

Statistical Inference (R3)

Jonathan von Schroeder
Vladimir Vutov
Pascal Rink
Serhat Günay
Anh-Tuan Hoang

One of the unique features of the 3rd RTG cohort is the incorporation of the additional 4th research area Statistics (R4). Hence, the current emphasis of the study groups is on the four research areas Dynamic Inverse Problems (R1), Direct Optimization (R2), Data Analysis (R3) and Statistics (R4).

As the third cohort of the RTG just started, the study groups are currently being organized. In the following, we list the first study groups which have been formed until now.