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challenges related to data reliability in monitoring networks, which can affect simulation models or related control systems. Collaboration with multidisciplinary teams will be essential, as the research
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
Infrastructure? No Offer Description The aim of this PhD is to model the development of microstructures during welding processes on thick parts, in the context of nuclear equipment, for which deposits of several
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energy system models based on the institute`s own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE. Your tasks in detail: Implementing geothermal plants with material co-production in
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Postdoctoral Positions in PFAS Analytics, Degradation, and Thermophysical Properties - DTU Chemistry
thermophysical properties vary across the diverse PFAS chemical space and how these properties may be predicted using computational models. These positions offer an excellent opportunity for early‑career
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; machine learning methods (i.e. supervised and unsupervised learning, deep learning, reinforcement learning, etc.); artificial intelligence methods (e.g., predictive modeling, natural language processing
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already been awarded a PhD degree. Selection process You should submit your CV through a dedicated site: https://cv.newton-6g.eu/ Additional comments Position: Data-driven models for CF networks
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applied in particular to the modeling of 3D-printed concrete at the Navier laboratory, to better predict complex phenomena such as material curing and crack formation. Where to apply E-mail jeremy.bleyer
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modeling – including predictive models for Alzheimer’s disease with a particular focus on sex-specific (female) risk. The harmonization pipeline will integrate female-specific and cognitive variables/items
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, aimed at uncovering the key traits that define successful microbial biofertilizers, and to develop predictive models that can guide the rational design of next-generation BioAg products tailored
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understanding of ML approaches for classification, anomaly detection, and prediction using high-frequency data. Experience with multilevel longitudinal data, missing data strategies, and clinical outcome modeling