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intersection of Mathematical Finance, Stochastic Analysis and Machine Learning. The research areas cover a wide range of challenging topics such as (infinte dimensional) stochastic analysis, affine and
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to the department Ph.D. program and will work on the development and analysis of statistical methods for machine learning, particularly in the context of high-dimensional models and with a particular focus on methods
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promise in any area of theoretical solid-state physics, including but not limited to modeling of oxides and their surfaces, machine learning methods, and/or development of novel solutions to solve the many
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need expert knowledge in bioinformatic data analysis. Strong expertise in multi-omics data analysis (using R and Python) and a deep understanding of machine-learning models are must-criteria
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spoken German/ willingness to learn German Computer skills: MATLAB and/or R desirable You are motivated and self-propelled You are flexible and creative You should be a team player with high social skills
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methods, especially quantitative methods Experience in learning methods of Computational Communication Science, e.g. computer-assisted text or image analysis, agent-based modeling and simulation, or network
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scientific and societal challenges. The project where the candidate is expected to contribute is about solving Singularly Perturbed PDEs with deep learning methods. Such equations arise in physical models