27 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation"-"St" positions at University of Twente (UT)
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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
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algorithms for optimal operation of grid-integrated LDES; Develop a co-simulation framework to analyse LDES performance under different grid scenarios. Collaborate with consortium partners to translate
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD project aims to develop a
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deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement
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within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
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require efficient numerical algorithms to be practical and to enable robust optimization. Therefore, in this project you will: Develop efficient numerical methods and strategies to solve the electromagnetic
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industrial machinery. However, achieving low friction and high durability remains a challenge due to complex interfacial phenomena. This project aims to develop innovative surface treatments and interface
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health, air quality and noise pollution. In the project Green and safe routing, the challenge is to develop, validate and test a method that offers personalized, tailored travel and route advice in order
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. students, working on all aspects of RF integration problems You will have ample interaction with multiple industries that are keen to apply your insights You are surrounded by the best (industrial
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adequately to available treatments. There is a pressing need for novel disease modifying treatments. At present anti-rheumatic drug development still relies on simplified in vitro model systems and on animal