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Field
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at Tilburg University, in collaboration with the Faculty of Military Sciences and the Joint Sigint Cyber Unit, invites applications for a fully funded postdoctoral position focusing on predictive modelling
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critical for predicting future changes to fisheries production and deep ocean carbon storage as the ocean warms. Biogeochemical-ecosystem models are currently used for this purpose but have difficulties: in
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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 14 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
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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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screening to a predictive, rational design of absorption media (WP1) and to validate their efficiency in VOC capture, with a specific focus on emissions from the semiconductor industry. (WP2). The project
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A postdoc position is available immediately in the laboratory of Dr. Daniel Blair which focuses on leveraging high-throughput chemical synthesis and MS/MS analysis to create predictive models
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Responsibilities: Develop a predictive model integrating the direct and indirect costs of urbanization. Calibrate this model using real data from specific case studies. Simulate urbanization costs based on various
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sustainable fluorination reactions. Under the supervision of Dr. Chris Ewels, a CNRS Research Director and expert in DFT modeling of nanocarbon materials, the postdoc will lead Work Package 2 (WP2), which aims
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models are deeply rooted in real-world biological data. The collaborative approach allows for the development of predictive models that bridge the gap between theory and experiment, with a focus on high