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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and
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. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and statistical multivariate methods to extract spatio-temporal spike patterns. Finally both results will be linked and
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field Proficiency in at least one programming language (Python, R, C++, Julia, …) Good analytical skills with a sound understanding of data evaluation Prior experience with single-cell data analysis
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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formulations Development of a screening platform for cosmetic formulations Data analysis by “Design of Experiments” (DoE) and other statistical tools Your profile Successfully completed graduate university