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-learning (ML)- driven and physics-based computational workflows to screen large molecular libraries, predict key electrochemical and physicochemical properties, and deliver ranked shortlists of high[1
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the definitive proof-of-concept for the MagHeat technology. -- [[1]](#ftnref1) https://energy.ec.europa.eu/topics/energy-efficiency/heat-pumps_en Where to apply Website https://www.academictransfer.com/en/jobs
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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doing their final assignment on sub-projects pertaining to the current research project. The PhD research will be performed in the Production Technology group. Where to apply Website https
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Mathematics (Inverse Problems), Computer Science (Machine learning, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of the NXTGen High-tech
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collaboration with a leading high-tech semiconductor manufacturing company, demonstrating how edge devices can execute foundation models with guaranteed performance and minimal energy footprint—closing the gap
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an import element in the prediction of reactor-scale operational scenarios providing compatibility to both, required heat and particle exhaust constraints and good fusion plasma core performance. Given
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that are indicative of a strong desire and ability to perform high-quality research in economic history or historical political economy Genuine interest in the research topic of the project Experience with collecting
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) Application Deadline 31 Mar 2026 - 21:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD position is part of RE:Match.D, which aims to develop a digital