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Field
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Dynamic Systems offers a position as Research Associate / PhD Student in FPGA Design for AI Applications (m/f/x) (subject to personal qualification employees are remunerated according to salary group E 13
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» Applied mathematics Mathematics » Computational mathematics Computer science » 3 D modelling Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 30 Sep
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD will be carried out under
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experience in astronomical data analysis activities, coupled with use of machine learning approaches to data validation, a good practical knowledge of scientific algorithm development and a PhD degree in a
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comprehensive capabilities in conducting sophisticated fluid dynamics data analysis. This job includes aerospace-related machine learning algorithms. Specific duties include data analysis from wind tunnel
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computationally intensive, relying on iterative time-marching algorithms to reconstruct heating environments. This limits their use in more advanced computational analyses, such as uncertainty quantification
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on the use of Machine Learning algorithms for rapid damage assessment. Research topics could focus on: the definition and use of novel damage sensitive features, physics informed machine learning, transfer
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learning algorithm for multi-omics integration 3) Maintenance of server / database (Linux environment) 4) Assisting other team members in data analytics 5) Presenting work in at least one conference in
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, cognitive science, art history, and creativity studies exploring digital interfaces that facilitate collaboration between humans and AI algorithms. We are located at the Department of Management at Aarhus BSS
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proofs obtained via state-of-the-art theorem provers and model-checkers. There will be a need to develop novel synthesis algorithms that can scale. There will be a need to fine-tune foundational models