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for transformers, map the benefits and risks of increasing the load and temperature limits, and pilot new concepts for risk control. The PhD Position belongs to Work Package 1 of KSP UPLOAD: ‘System-level Benefits
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for the development of more predictive preclinical platforms. This PhD project is part of the ANR JCJC EXOFLEX project, which aims to develop an instrumented microfluidic platform capable of simultaneously controlling
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Research Network – Krakow Institute of Technology Position: PhD Student – Scholarship Holder in the FIRST TEAM FENG Project Łukasiewicz Research Network – Krakow Institute of Technology announces an open
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detection, real-time data analysis and/or predictive modelling, particularly for dynamic and safety-critical environments. Interest in AI sustainability, including resource efficiency, scalability
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regularly in its various training and research activities, as well as the weekly seminars in IDAEA-CSIC. Where to apply Website https://www.idaea.csic.es/job-offer/phd-position-understanding-and-predicting-n
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2 PhD Positions in Computational Modelling of Biological Membranes and Adaptive Functional Materials
at the University of Münster invites applications for two fully funded PhD positions in computational modelling of biological and functional material. The two projects address complementary questions in biological
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an entirely open-loop manner, with no real-time measurement of the microstructure being produced. This PhD will develop and demonstrate, for the first time, a closed-loop EM-based microstructure control system
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of Marine Technology at NTNU has a vacancy for a PhD Candidate in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures. The position is part of the AIMOS project (Artificial Intelligence
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predictive models of formation alteration. The project is funded by Eni S.p.A through the UK Energy Futures (ukenergyfutures.org ). research partnership, bringing together geoscientists, engineers, and social
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, these flows remain poorly understood. As a result, even the most basic properties cannot be predicted reliably. For instance, the best available models over- or underestimate the measured pressure drop in a