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Posting Description POSTDOCTORAL ASSOCIATE, Chemistry, will lead design and development of an open-source Python-based package for the prediction and analysis of spectroscopic data, primarily
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imaging data, preferably using Python, and hands-on experience building bespoke laboratory setups for imaging experiments investigating alloy composition or relevant applications. You will have a strong
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LabVIEW scripts and APIs for target-specific applications, working with data acquisition (DAQ) systems, and proficiency in MATLAB, Python, and LabVIEW. Capable of working independently and as part of a
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Experience with scientific programming (e.g. in python) Experience with modelling of disordered materials is seen as an advantage Experience with automated computational workflows is seen as an advantage
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and Edge Computing Good programming skills (e.g., Python, MATLAB, C++) and experience with system simulation. Experience with industrial field projects or familiarity with DCS/MES systems is preferred
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skills in R and/or Python and experience in statistical modelling of high-dimensional biological data. Experience working in HPC environments and managing large-scale sequencing datasets is essential
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the JAX Python library, including efficient implementations of classical numerical algorithms. 2. Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation
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collaborative research. Where: Leipzig Start: May 2026 Details: https://uni-leipzig.b-ite.careers/jobposting/a9bfd0f27d1f4ddc2404831df4… Where to apply E-mail dekan@physes.uni-leipzig.de Requirements Research
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application to power systems, control, optimization, or cybersecurity. Programming skills in Python and experience with at least one modern machine learning framework such as PyTorch or TensorFlow. Demonstrated
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islet imaging assays, in vivo assays of metabolism (e.g. glucose tolerance tests), and developing novel machine learning models (in Python and R). Experience with mammalian cell and tissue culture