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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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Deadline Continuously Employer Municipality Scope (%) Duration Place of service Loading...
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for Real-World Optimisation and AI Applications Brain-Computer Interfaces & their Applications Computational Neuroscience: Reinforcement Learning and Microzones in the Cerebellum Explainable Generative
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passionate about sustainability, clean energy, and water treatment technologies?Are you skilled in machine learning, process modelling, or chemical engineering simulations?Are you excited by the idea
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(supervisor) The position holder will be required to perform the following tasks/will do research on the following topics: Software engineering practices for machine learning Tabular machine learning Large
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Are you passionate about dynamical systems and system theory? Are you interested in making the design process of complex dynamical systems simpler by smart new tools for systems engineering? Are you
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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, engineering, physical sciences, economics or any other field where evidence is provided of sufficient computing and mathematical background) solid experience of programming, machine learning methods and ideally
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap