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materials theory and design. Where to apply Website https://www.academictransfer.com/en/jobs/355040/postdoc-on-machine-learning-and… Requirements Specific Requirements PhD in physics, materials science
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the electronic structure and to realise artificial matter with exotic electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is
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”. Are you excited about the opportunities that data science in nutrition—especially the role of targeted metabolic modelling, machine learning and AI offer in developing effective personalized nutrition
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Master’s degree in data science research, or a comparable domain. A strong background in machine learning (specifically generative models) and/or symbolic reasoning. Ability to conduct high quality academic
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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
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training programs for PhD candidates and consortium members in interdisciplinary and transdisciplinary research methods; Develop and facilitate workshops and webinars to build collaborative expertise across
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such as data science, AI, computer science, machine learning, Earth system science, climate etc., with a thesis subject relevant to the description of the tasks outlined above. Additional requirements In
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above. Education in related fields such as data science, AI, computer science, machine learning, space engineering, Earth system science, climate, etc. would be an asset. Additional requirements In
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Village to calibrate and validate models. Investigating control strategies (e.g. predictive or machine learning approaches) to improve performance and reduce costs. Collaborating with industrial partners
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Technology Enabling Advanced Drone- Facilitated Active Support Tactics for Military and First Responder Operations.” You will be a member of the Human-Robot Collaboration Lab and Learning & Autonomous Control