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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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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
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across subsets of neurons to low-dimensional manifolds of high-dimensional space of population neuronal firing rates. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and
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of the original computations at a fraction of the cost. This hybridization aims not only to accelerate performance but also to maintain, if not improve, analytical rigor. The improved modules will be integrated
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Your Job: This PhD project aims at relating precisely timed spike constellations across subsets of neurons to low-dimensional manifolds of high-dimensional space of population neuronal firing rates
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disciplines (meteorology, environmental science, high performance computing, software development and data science). Your Profile: Mandatory qualifications are: M. Sc. degree in meteorology, physics
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to accelerate performance but also to maintain, if not improve, analytical rigor. The improved modules will be integrated into an updated analytical pipeline and validated against benchmark datasets drawn from