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analysis (e.g. regression, correlation, multilevel modelling, structural equation modelling), and advanced analyses (machine learning). Attends trainings and skill development workshops as necessary
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solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/human-organs-on-chips/ What you’ll do: Independently
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 21 days ago
to intertwine a multi-contact whole-body controller, a digital simulation of the interacting humans, and machine learning models to predict and respond to human movements and intentions. In a crescendo of
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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into the School's activities. We are particularly interested in candidates with expertise in Digital Health and AI in Medicine, including machine learning (especially deep learning), natural language processing, and
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4 Apr 2026 Job Information Organisation/Company University of Cyprus Department Electrical and Computer Engineering Research Field Engineering » Control engineering Engineering » Computer
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 2 months ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani
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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
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will apply machine learning — in particular physics-constrained symbolic regression — to discover compact analytical spin-Hamiltonians and their parameter dependencies. These Hamiltonians will feed large