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part of a larger EU project entitled “FEDORA - Federation of network optimization services, simulation foresights, and data alchemy for adaptable, agile, secure, and resilient multimodal traffic
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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access to state-of-the-art facilities and expert assistance with improving your proposal writing skills and feedback on grant applications. We actively facilitate building your professional networks, both
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to conduct world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring
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, mentoring, research dissemination, networking, manuscript drafting etc. Engagement in cutting-edge research in an emerging area of metabolism, with high potential for discovery and future applications
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languages such as Python or R. Experience with machine learning, systems biology, or network modeling approaches. Previous expertise in human cardiometabolic or complex diseases, with domain expertise in but
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fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results
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controlling cancer cell biology. Your research focus will be on (1) the molecular mechanisms underlying the action of the oncoprotein MYC; or 2) the role of chromatin remodeling complexes in regulating access
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for light trapping in thin-film solar cells .” You will become part of an enthusiastic team working closely with collaborators at DTU Physics and DTU Nanolab to advance neural network-based methods
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physics and condensed matter theories to address the problem of fracture in complex materials. You will be working with experimental model systems and numerical simulations of materials that exhibit