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); Doctorate in Physics, Computer Science or related fields (for Post-Docs). The positions are funded via the Cluster of Excellence (Machine Learning for Science), the ERC Starting Grant ArtDisQ and the
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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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biophysics. Familiarity with simulation environments, numerical methods, or machine learning approaches is an advantage. Fluent command of written and spoken English is necessary; German is an advantage but
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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of potentially novel modes of protein binding is possible in collaboration with other members of the lab. Desired (but not absolutely required) skills: programming in python, machine learning, and experience in
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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performance in fuel cell (biogas) and co-electrolysis applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under