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regions. Machine learning provides a promising route to capture these relationships more systematically by identifying how local geometric features determine the resolution required for reliable prediction
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and scalable. Design and build a technology demonstrator prototype of clinical-testing grade. Collaborate with interdisciplinary teams, including clinicians, engineers, and machine learning (ML) and
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combines synthetic biology, machine learning, and biosensor and pathway dynamic regulation design to produce next-generation bio-based chemicals. The Computational Synthetic Biology group (CSBG), led by Dr
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administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate: 01.02.2026 | Working hours
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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study cycle or non-award courses of Higher Education Institutions. Preference factors: Programming experience in Python; Knowledge of machine learning and computer vision Minimum requirements
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, machine learning tools, and simulation techniques. If you thrive at the intersection of engineering, data, and advanced computational science, this position will allow you to contribute meaningfully
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based