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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
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Your position New computational and analytical tools in population genetics allow to estimate both contemporary and past effective population sizes. With these in hands, the project aims to verify
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, the work will focus on the design and fabrication of functional electrode coating materials (nano-enzymes) and surface architectures that enable reversible analyte binding, high signal stability, and
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paramagnetic resonance (EPR), or magnetic resonance imaging (MRI) Familiarity with analytical tools: X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray computed tomography (XRCT) Expertise in
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at the University of Basel, Switzerland (https://duw.unibas.ch/en/ecoevo/ ), is looking for a PhD student in the field of Conservation Genetics. New computational and analytical tools in population genetics allow
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well as strong analytical and mathematical skills Previous experience in timber construction or structural engineering will be beneficial Fluent English language skills are required for this position, German