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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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. Your application will receive fair consideration. Challenge. Change. Impact! Faculty Mechanical Engineering From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted
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systems modeling, or an [electrical] engineering background, but with an interest (or prior experience) in computational intelligence, AI and/or agent-based modeling techniques. He or she will work closely
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18 Oct 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Civil engineering Engineering » Water resources engineering Environmental
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and magnetic fields. A crucial aspect lies in the interpretation/modelling of the results. In this role, you will get to work with a variety of devices structures, with an emphasis on quantifying
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computational design. Good programming skills with experience in reduced-order modelling techniques and data-driven symbolic regression tools is a plus. TU Delft (Delft University of Technology) Delft University
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these models in real tissue context, revealing how spatially organized cell communities rewire under genetic perturbation. Key challenges Use and improve single-cell foundation models to predict SNP-driven
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Biomechanical Engineering, biomedical engineering, technical medicine, or a related field. Strong interest in musculoskeletal modelling, human experimentation, and medical imaging. Solid programming skills (e.g
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Harnessing Nonlinear Dynamics: From Data-Driven Discovery to Engineering Job description Nonlinear dynamics lies at the centre of many mechanical systems, from large-scale structures to nanoscale