24 computing-"https:"-"IDAEA-CSIC"-"https:"-"https:" Postdoctoral positions at Aarhus University
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The Department of Computer Science at Aarhus University invites applications for a 24-month Postdoctoral Research Fellow in Explainable AI interested in interdisciplinary research between
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developing optimization-driven approaches to multimodal device tailoring. We are looking for someone with A PhD in Human-Computer Interaction or a closely related field Strong programming skills (e.g., Python
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multidisciplinary and multicultural team. Inclusive and open minded. Who we are Further details about the Software Engineering and Computing Systems Section can be found here: https://ece.au.dk/en/research/key-areas
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The Center for Sustainable Energy Materials (CENSEMAT) at the Department of Chemistry, Aarhus University, invites applications for a 24-month postdoctoral position in computational materials
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Aarhus University (www.chem.au.dk) is one of the leading European chemistry departments with a broad research program. It has a permanent staff of 43 full and associate professors, a support-staff of ~40
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addition to supporting user operations and SCS, the successful candidate will have the opportunity to pursue a research program under the supervision of the SINCRYS beamline scientist. The commissioning of a new
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data. Statistical analysis using R and/or Python. Reproducible computational workflows. Scientific writing and publication. Microbiome research and host-associated microbial communities. The ideal
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data, variant discovery and filtering, and downstream population-genetic and evolutionary-genomic analyses. The work will require substantial data processing, quality control and computational
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, membranes, neuroscience and personalised medicine. The Department of Biomedicine provides research-based teaching of the highest quality and is responsible for a large part of the medical degree programme
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. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design