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deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery. Submit a
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learning and deep learning methods to analyze multi-omics data (genetic, epigenetic, transcriptomic, imaging, single-cell genomics and spatial omics data) with the goal of understanding the underlying
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- Develop original numerical methods for facility simulation in presence of expansion waves - Demonstrate improved estimates of rate constants for two-temperature models - Contribute
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Biochemistry, Molecular/Cell Biology, and/or BioEngineering documented by peer-reviewed publications Skills in a broad range of methods in the fields of Biochemistry, Molecular/Cell Biology, and/or
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology
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, PETS has developed into a major research group, recruiting numerous new core, part-time, and adjunct faculty, and an experienced team of highly trained administrative and analytic staff. The current PETS
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challenging task. In most cases, only a sample of a network is observed. Therefore, network completion needs to be addressed. Matrix completion methods have proved to be efficient when reconstructing a non
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the Research Group “ Numerical Mathematics and Scientific Computing“ (Head: Prof. Dr. V. John) starting September 1, 2025. The position is assigned to the research project "Randomization of Surrogates
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responsible for conducting numerical and theoretical studies of turbulent flows and their associated geometric structures, focusing on passive and active scalar fronts, multiphase mixing layers, and
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or BioEngineering (or equivalent) Solid background in Biochemistry, Molecular/Cell Biology, and/or BioEngineering documented by peer-reviewed publications Skills in a broad range of methods in the fields