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, robotics, and machine learning. You will work within a multidisciplinary supervisory team spanning engineering, robotics, and computer science, and collaborate with researchers working on real-world
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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machine learning (ML) and artificial intelligence (AI) workflows, the project aims to create a comprehensive molecular atlas and identify novel, translational biomarkers and therapeutic targets. Project
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learning, or reinforcement learning from human feedback - Ability to work independently and collaboratively in an interdisciplinary team of clinicians, computer scientists, and biologists - Excellent
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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also study professional learning in relation to the collaboration and innovation processes of professionals, particularly so in multidisciplinary settings and new forms of organizing (such as learning
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learning with interactive exhibits in public spaces. The PhD student will collaborate closely with visualization researchers, AI-specialists, research engineers, educational scientists, and pedagogues
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About the project: From Brittle to Ductile: Machine Learning 3D Fracture Simulations for Extreme Environments Supervisor: Prof, James Kermode, University of Warwick Develop cutting-edge machine
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innovation player in Luxembourg, dedicated to technological innovation in the fields of environment, information technology, and materials. At the heart of a collaborative ecosystem spanning fundamental
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning