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Field
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, 100% funded PhD student position to fill starting around June 2026. Research is to be in the field of computational methods in nonlinear and large scale optimization / inverse problems or in novel
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RESPONSIBILITIES: Implementing laboratory and computational workflows for large-scale biological studies Collecting experimental and high-throughput data Characterisation of molecular and cellular mechanisms
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interdisciplinary journals and conferences, and contributing to open-source research software. The developed methods will be validated using large-scale electron microscopy data from collaborative research projects
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, catalysis studies - in particular electrocatalysis and Python programming Experience within the following fields is highly beneficial: Transmission electron microscopy, Electrochemistry, Image analysis, Big
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climate because they interact with radiation from the sun and from the earth. However, the aerosol-cirrus interaction in the UTLS is not well understood because of lacking sufficient in situ data. In
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mining. In-depth knowledge of the design, analysis and implementation of algorithms for large text corpora, including efficient data pipelines and clean experimental design. Strong NLP skills for semantic
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(4DSTEM). This approach will combine three-dimensional charge distribution data, generated through atomistic simulations, with machine-learning-driven modelling to guide and refine the phase reconstruction
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field * Strong background in mathematical and computational sciences * Experience with large-scale machine learning, foundation models, or data-centric AI is a plus * Driven, with a strong work ethic and
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design recovery and stability strategies using large-scale simulation workflows. Build and expand realistic, continent-scale power system models (e.g., the European transmission grid). Implement and test
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project "Information Field Theory for Experiments at Large Research Facilities" has a PhD opening in the field of data-intensive radio astronomy. Methods of information field theory will be used