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have a 1st class degree (BEng or MEng/MSc) in electrical/mechanical engineering. Expertise in numerical tools (Ansys, JMAG, .etc) and programming are desirable. Experience in electrical machine
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thermochemical TES. Your main supervisor will be Prof Adriano Sciacovelli and you will join the Thermal Energy Section at DTU Construct. Your work will contribute to a paradigm shift in how complex TES systems
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the following skills and qualifications (tailored to the specific project): Driven individuals who want to be a part of a world class team Some familiarity in healthcare or engineering/image based analysis
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create a computational tool based on experimental input, simulated data, and machine learning methodology to extract 3D atomic structure information from 2D identical location STEM images. STEM image data
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. David Marlevi, Prof. Ulf Hedin, and Dr. Ljubica Matic to improve stroke risk prediction for patients with carotid atherosclerosis using a multidisciplinary combination of data-driven imaging
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mathematical statistics (University of Gothenburg / Chalmers University of Technology) Prof. Mats Nilsson, pioneer in spatial genomics (SciLifeLab & Stockholm University) Integration into the national DDLS
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molecular cell identification and single-/two-photon imaging techniques. You will work at the Leibniz Institute for Neurobiology (LIN) with Prof. Stefan Remy and in close cooperation with Dr. Janelle Pakan
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, and the preparation of highly non-classical mechanical quantum states. Who are we looking for? We are looking for candidates within the field(s) of physics or related engineering disciplines. Applicants
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Familiarity with large language models or multimodal systems An interest in visual reasoning, educational technology, or human–AI interaction Experience with neural networks for image or video understanding
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of generative models by introducing a training regime inspired by the Thinking, Fast and Slow paradigm. Recently, the use of RL has been shown to significantly improve the performance of LLMs. The goal