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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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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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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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or equivalent degree in Biology, Immunology, Biochemistry or a related discipline. Alternative: MD with a strong interest in basic research Enthusiasm for joining basic research with clinically relevant issues
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! The template can be found at: https://www.daad.de/medien/deutschland/stipendien/formulare/recommendation.doc [doc-Datei] Application deadline : The deadline for your application is 15 October 2025 at 00
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subsurface leaching into groundwater within agricultural systems experience in statistical analysis of research results or willingness to acquire such as well as to complete a PhD degree self-motivated
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with scientific programming (e.g., MATLAB, LabView) is advantageous Knowledge of scientific instrumentation and electronics is desired Experience with (or willingness to learn) stop-flow spectroscopy and
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ranges from core areas of computer science and electronics over medical applications to societal aspects of AI. SECAI’s main research focus areas are: Composite AI: How can machine learning and symbolic AI
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defects of smectic-liquid crystal order in developing cross-striated muscle, or use machine-learning to expand existing custom-built image analysis pipelines (Python, Matlab). To learn more about this