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Field
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algorithms to compute similarity between interaction interfaces across millions of comparisons. This hinders identification of novel modes of protein binding, i.e. those predicted by AlphaFold, and it hinders
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, particularly insect decline and species distribution models (SDMs), are desirable • Experience with and knowledge of a programming language commonly used in data science (e.g., R or Python) • Experience and
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ecological modelling, particularly species distribution models (SDMs), are desirable LanguagesENGLISHLevelGood Research FieldEnvironmental science » Ecology Additional Information Work Location(s) Number
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groups are strongly encouraged to apply. Your Tasks: Development and application of algorithms for modelling, evaluation and visualization of ultrafast processes Investigation of ultrafast dynamics in
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, including rare and extreme events. Quantify flood risk and improve early warning predictability in out-of-distribution conditions (climate change, land cover changes), and use explainable and causal ML
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Computational Biophysics/Chemistry (see also https://constructor.university/comp_phys ). The PhD position is focused on efficient algorithms for the simulation of non-adiabatic exciton transfer dynamics in light
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exploration, optimization, and search algorithms in extremely complex and enormously large spaces motivated by physics and chemistry (RL, BO, Large-Scale Ansatze, …) AI-driven discovery of hardware for some of
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research