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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects and mentoring. Contribute to academic publications
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, including prediabetes, type 1 and 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). Their research informs artificial pancreas algorithms and contributes to drug development targeting hepatic
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. Responsibilities: Conduct research on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects
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profile. The successful candidate will primarily advance data analysis, algorithm development, modeling, and translation of complex measurements into practical decision-support, process-optimization, and
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will also investigate process optimisation algorithms, providing a route toward autonomous optimisation and ultimately enabling long term stable operation under variable load conditions. This post is
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to have a Ph.D. or equivalent in evolutionary biology, genomics, or related fields. Candidate must have excellent computational and bioinformatic skills as well as strong experience with next-generation
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-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs. The successful
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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Researcher position. The selected candidate will work on research and development for testing, analysis, and integration of AI and machine learning (ML) algorithms in new simulation and training architectures
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research grants. These goals include learning about demographic history or natural selection from genetic variation data, or understanding genetic architecture and evolutionary history of complex traits in