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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI
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coarse grained reconfigurable arrays (CGRAs), virtualisation of FPGAs using partial reconfiguration, and accelerator support for machine learning. Postdocs at KAUST enjoy generous salaries and free
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intelligence, available at the Department of Informatics, in the Scientific Computing and Machine Learning (SCML) research group. Starting date as soon as possible/by agreement. The fellowship period is three (3
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Johns Hopkins Medicine - Laboratory of Human Biomimetics | Saint Petersburg, Florida | United States | 26 days ago
laboratory automation COMSOL Multiphysics, fluid mechanics, heat transfer, or mass transport CAD, additive manufacturing, 3D printing, or machining Bioreactors, microfluidics, or organ-on-chip systems
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publications, commercialisation, and/or dissemination as open-source designs. The ideal candidate might be from an academic (e.g. PhD/Postdoc in a related engineering/electronics/sustainable engineering area) or
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) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
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an important challenge in Human-Computer Interaction research: how can we create a proxy for a remote physical object that does not replicate, but extends the experience of interacting with the object itself
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fluid mechanics, computational geometry, meshing, computational graphics, computational vision, or scientific machine learning in general. Successful candidates will join a community of researchers in
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analyzing hyperspectral data and developing machine learning models - Genetic or molecular lab experience - Bioinformatics, or statistical genetics experience - Excellent written and oral communication skills
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expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques. • Strong expertise in discrete choice modelling, statistical modelling, and