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will be at the absolute forefront of combining GNSS and modeling the different contributions that courses solid Earth deformation where the main contributors are elastic deformation, glacial isostatic
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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potential for exploiting temperature gradients for producing electricity and predict their long-term performance under real operating conditions. The project also includes modeling of heat transfer and
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operate experimental facilities (electrolysis, PCM, BESS, etc.) and develop tools related to PtX testing, modelling, control, and energy system integration Co-supervise BSc, MSc, and PhD students and
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interest in information processing in humans and computers, and a particular focus on the signals they exchange, and the opportunities these signals offer for modelling and engineering of cognitive systems
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models Tough bio-inks for surgical procedures We are looking for candidates with a high degree of independence and a strong drive for scientific excellence. These positions provide excellent opportunities
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writing scientific papers. The developed models will be tested on data from energy investment models, as well as transport infrastructure problems. We will be an academic team of three PhD students and four