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the full complexity of fabrication processes and enable optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication
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systems capable of selecting and combining models of different complexity, in order to better represent groundwater dynamics and improve large-scale predictions under climate change. Objective — The PhD
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methodologies for modeling and predicting the properties of complex materials and surfaces, including doping and/or functionalization to optimize their physicochemical characteristics Where to apply Website http
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research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Research Engineer (Modelling and
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are planned for participation in several international conferences). Expected skills Knowledge and technical skills: • Master's degree (or engineering degree) in Evolutionary Biology, Ecological Modeling
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modeling in cardiovascular science through high-fidelity, large-scale simulations. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional
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of estimators, test statistics, and predictions modelling dynamic relationships between brain activity and subjectively experienced pain at the individual level Research context The PhD is closely linked
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charging) Predictive charging planning algorithms Railway Smart Pricing models Techno-economic evaluation versus the traditional catenary model This is applied research with validation in simulated
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Plant species distribution models (SDMs
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 13 days ago
to improve the prediction of electric vehicle (EV) mobility patterns, energy demand, and state of charge (SoC) over time and space. The work will include the design of advanced modeling frameworks integrating