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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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large language models (LLMs), natural language processing (NLP), and/or machine learning, with a verifiable track record (e.g., publications, thesis, or open-source contributions). Proficiency in Python
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Can technology learn to listen to how athletes feel, and not just to what the sensors measured? In the ASPIRE project, we develop knowledge for the new generation of sports tracking technology that
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multiple research tracks. You will work closely with three PhD candidates focusing on different clinical applications, while leading developments in acquisition, probe localisation, adaptive imaging, and
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the sensors measured? In the ASPIRE project, we develop knowledge for the new generation of sports tracking technology that integrates athletes’ subjective experiences (such as perceived exertion, motivation
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researcher, you will play a central role in this ambitious programme and act as a technological integrator across multiple research tracks. You will work closely with three PhD candidates focusing on different
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measured? In the ASPIRE project, we develop knowledge for the new generation of sports tracking technology that integrates athletes’ subjective experiences (such as perceived exertion, motivation, enjoyment
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materials, and nature-inspired design. Job requirements You hold a PhD in Civil Engineering, Materials Science, Mechanical Engineering, or a closely related engineering discipline, with a proven track record