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to the development of our algorithmic approaches and also contributes with the implementation of novel methods for the detection of cancer in tissues. Knowledge and in best case hands-on experience in handling images
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microscopy, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand
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with mathematical modeling and algorithmic insights Counterbalancing issues such as bias, overfitting, and inexplicable errors in excessive pre-training: by making systems explicitly aware of the reasons
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will join a growing and internationally active research environment characterized by strong academic excellence and close industrial collaboration. We offer: A research environment focused on distributed
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pruning), carbon-aware computing, minimizing algorithmic complexity, maintenance requirements, mapping energy efficiency and related aspects using KPI (key performance indicators) with respect to ESG
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academic excellence and close industrial collaboration. We offer: A research environment focused on distributed AI systems, digital twin architectures, and scalable software infrastructures for energy
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monitoring bioelectrochemical reactors - mapping microbial distributions across reactor niches - functional omics and targeted assays for key taxa/functions - establish standards of practice in
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systematic investigations of charge state distributions, fragmentation behavior under collisional- and electron-based dissociation, and differentiation of peptide isoforms and PTMs using negative ion mode
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of machine learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics
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faculty determines the distribution of the various assignments. The weighting of the different assignments may vary over time. Employment as a postdoc requires scientific qualifications at PhD level at