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classification algorithms need to be extended with the capability to detect out-of-distribution environments and to autonomously infer their traversability. In this thesis, you will design a semantic
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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in Summer 2026, for a term of 2 years with the possibility of an extension. The postdoc will join the ERC-Starting Grant project team on “Participatory Algorithmic Justice: A multi-sited ethnography to
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silico approaches. This may include mathematical modeling of biological systems, machine learning and artificial intelligence methods, and the development of innovative algorithms and software pipelines
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already available and validated. Accordingly, the focus of the thesis will be less on the chemical processes in the reactor and more on the use and adaptation of suitable ML and optimisation algorithms in
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as of the 01.04.2026 at the following conditions: 50% = 19,92 hours Pay grade 13 TV-L limited by 31.03.2029 Your tasks: Development of architectures and algorithms for adaptation of time-triggered
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neuroscience to gain a deeper understanding of the principles and algorithms underlying the computations of visual systems and enabling “robust vision.” The aim is to investigate the fundamental principles and
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systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models