Phd Position in The Field of Optimization and Deep Learning Theory
Vor 3 Tagen
The Optimization of Machine Learning Systems Group (Prof. A. Lucchi) at the Department of Mathematics and Computer Science at the University of Basel is looking for several PhD students in the areas of optimization for machine learning, and theory of deep learning. Potential research topics include the development of adaptive optimization methods, the exploration of the loss landscape in deep neural networks, and the design of novel neural network architectures that enhance efficiency in both training and inference.
Position available starting around November 2025 (position may begin later as well). The duration of a PhD in Switzerland is 4 years.
**Your position**
- Conduct research in the field of applied mathematics and computer science
- Write research papers, articles, and present results at leading international conferences. We publish in venues such as ICML, NeurIPS, ICLR, JMLR, AISTATS, SIAM journals, Mathematical Programming, and more
- Interact with national and international teams of mathematicians and computer scientists
**Your profile**
Excellent communication skills and fluency in English (spoken and written) are required.
We also seek to increase the number of women and other groups in areas where they are underrepresented and therefore explicitly encourage such groups to apply.
**We offer you**
The position will be based in Basel, Switzerland. In addition, we also offer the following:
- Close supervision of your research, working with and access to international collaborators
- A diverse range of tasks in a dynamic environment
- The possibility of traveling to international conferences to meet with distinguished scientists
- Excellent salary, benefits, and working conditions
**Application / Contact**
- Short motivation statement (maximum 150 words) explaining your interest in the position and justifying why your background is a good fit for the position
- Curriculum vitae (including a list of publications if applicable)
- Scanned transcripts of bachelor's and master's degree (if already available)
- Contact info (no direct recommendation letters) for peers who can recommend you
- Your latest thesis (and/or one of your publications if applicable)
Apply
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