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Field
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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Biomedical Engineering conducts leading research in image analysis, computer vision, and machine learning, with a growing emphasis on generative AI and AI for scientific discovery. Our mission is to develop
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projects Designing for complex or high-consequence systems Human-centred product, service, or interaction design in healthcare, defence, or critical infrastructure Applied human-computer interaction or
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their applications to problems in natural sciences (physics, chemistry) and practical computing (machine learning, AI, optimization), and methods to make them compatible with near-term quantum devices
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 8 days ago
, Applied Mathematics, Physics, Biophysics or a similar qualification Interest in and/or experience with AI/machine learning A willingness to learn and extend this knowledge and master new challenges in
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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involve leveraging advanced natural language processing and medical image analysis to transform imaging data into clinically relevant information. Additionally, it will explore the use of multimodal fusion
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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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. The project will be based in the AI Laboratory for Molecular Engineering (AIME) , led by Assistant Professor Rocío Mercado Oropeza, where researchers develop new machine learning (ML) methods to tackle
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High