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Student Projects: Theses, Term Papers, and Project Groups

To handle the large number of requests for student projects, future bachelor’s and master’s theses, as well as research projects by doctoral students, are supervised by doctoral students who provide feedback to Prof. Möller at longer intervals. The following table lists our current group members, their availability for supervising theses, and the topics/areas in which they offer thesis supervision. If you are interested, please contact us at cv_student_projects (at) eti.uni-siegen.de and answer all the questions listed below. We will then assign you a supervisor. Please note that prior knowledge in our research areas (e.g., through participation in the chair’s lectures) is a prerequisite for writing a thesis with us (in particular, a completed course in machine learning or deep learning is required)! We do not offer internships.

 

PhD Available Slots Topic / Area
Alexander Auras Fully occupied Hybrid Approaches to Inverse Problems and Learned Regularization Methods
Jan Philipp Schneider Fully occupied i) Joint optimization methods for (physical) systems and neural networks,
ii) Neural rendering and scene reconstruction methods
Michael Schopf-Küster Fully occupied i) 3D shape completion;
ii) Theoretical studies on neural networks
Dr. Natacha Kuete Meli 2 slots available Quantum Computing for Computer Vision
Paolo Zuzolo Fully booked

i) Geometry processing

ii) Spectral methods

iii) Geometric Deep Learning

 

Questions

  1. What degree program are you enrolled in? Would you like to write a term paper, a group project, a bachelor’s thesis, or a master’s thesis?
  2. Which of the open topics (see table) are you interested in?
  3. Which courses in the field of machine learning have you already taken?
  4. Please attach your academic transcript (this is the list of your courses and grades, which you can access via unisono). You may also attach your resume (this is welcome but not required).
  5. Which courses are you currently taking that aren’t yet listed on your academic transcript?
  6. What ML-related projects have you worked on so far (in Siegen or elsewhere)?
  7. If you’ve collaborated with our group or another group as part of a project or as a student assistant, you can provide a reference.

    For questions 8–11, please rate your knowledge (0: never heard of it, 10: expert) in the following areas. In particular, to demonstrate your programming experience, you can provide us with your GitHub account, for example:
  8. Programming skills:
    i) Python,
    ii) deep learning frameworks (such as PyTorch, TensorFlow, ...),
    iii) C/C++,
    iv) MATLAB.
  9. Ability to read and understand a scientific paper or a deep learning publication.
  10. Ability to get someone else’s code (e.g., from a GitHub repository) to run.
  11. Formal background in linear algebra, optimization, and general mathematical topics.
     

Industry Theses

Industry theses are a special case and are not frequently offered by our group. One reason for this is that the objectives of industry theses often do not align directly with our research. We are particularly interested in publishing papers and/or defining projects that are well-suited for doctoral dissertations (with external funding). Therefore, we decide on industry theses on a case-by-case basis, with the following aspects being decisive:

1. Our group does not supervise theses under a non-disclosure agreement (NDA), except in exceptional cases of long-term collaborations with clear and realistic (short- to medium-term) prospects for how the group will benefit from the work, e.g., in the form of grants or joint applications for third-party funding.
2. Please discuss the topic and timeline with us as early as possible! The topic is assigned by the university, not by the company!
3. The goal of the master’s thesis is to demonstrate the ability to conduct scientific research, i.e., to develop previously unknown insights and/or new approaches to specific problems. Naturally, a thesis completed in industry must meet these scientific requirements in the same way as a thesis written at the university.