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processing techniques and machine learning methods to not only suppress interference but also leverage it to enhance sensor performance. The PhD student will play a key role in advancing these new radar signal
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algorithms to minimize interference. This project, in close collaboration with industry partners, will explore both signal processing techniques and machine learning methods to not only suppress interference
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no more than three years prior to the application deadline*. A working knowledge of advanced methods in High-Energy physics, in particular quantum field theory and particle physics is required. Familiarity with
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networks (CNNs), which identify local correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit
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correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit neural representations (INRs
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Mathematics,' and 'Computer Vision and Machine Learning' at the Faculty of Engineering, as well as Mathematical Statistics, which is cross-faculty. The position is located at the Division of Mathematical
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well as nuclear physics. This diversity of research topics allows us to connect fundamental questions about the particles and forces governing our Universe to energy-related research. The methods of our
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and written. Solid skills in computer programming (Python / Matlab). Experience with CAD and CAE tools. Knowledge of computational fluid dynamics (CFD). Knowledge of finite element method (FEM
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, you will be trained to think critically and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the
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' at the Faculty of Science, 'Algebra, Analysis, and Dynamical Systems,' 'Applied Mathematics,' and 'Computer Vision and Machine Learning' at the Faculty of Engineering, as well as Mathematical