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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
phenomena. New meta-model architectures based on learning may be proposed and tested on complex EDF use cases. However, this is not sufficient: can such a surrogate, learned from simulation data, predict
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and computational chemistry whose research lies at the interface of reactive molecular simulations and data-driven modeling. The successful candidate will develop and apply data-driven methods
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model disease trajectories to identify risk factors and improve disease prediction and prevention. Responsibilities will include conducting detailed analysis of multi-modal data from the UK Biobank, in
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The overall objective is to develop a predictive model and aging laws
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-Informed Generative AI for Causal and Dynamical Modelling in Multimodal Biomedical Research, a Horizon Europe project developing mechanism-informed generative AI for biomedical research. The post holder will
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between pollution control efficiency, electrochemical yield, and energy recovery potential; • Develop coupled electrochemical and hydrodynamic models to predict process behavior; • Participate in
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not limited to) identifying effective tutoring practices, running experiments on AI tutors in simulated and real-world environments, fine-tuning AI models to classify qualitative data, and building
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prediction, control systems (e.g., PLC), ML model deployment, and time series sensor data analysis are assets. • Excellent communication skills and ability to work in a multidisciplinary team. Duration of
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will rely on: Developing machine learning-based surrogate AI models (physically informed neural networks) to predict the evolution of calcium carbonate precipitation rates associated with the reduction
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Medicine Postdoc Appointment Term: Fixed term for one year, with opportunity for renewal Appointment Start Date: September 2026 Group or Departmental Website: https://med.stanford.edu/neonatology.html(link