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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 6 days ago
candidate will join the Inria centre of the University of Lille and be part of the LOKI research group, specialized in Human-Computer Interaction. It is affiliated with the CRIStAL laboratory (UMR 9189) and
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in international research visits if needed. We are looking for a highly motivated researcher with: A PhD in machine learning, computer vision, remote sensing, glaciology, climate science, or a related
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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needed. You have experience in advanced bioinformatic analyses of omics datasets, preferably single cell and/or long read RNAseq data. Demonstrable experience in advanced machine learning and/or high
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array
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strategies: Leveraging traditional and causal machine learning approaches to determine which patients are most likely to benefit from specific therapies. Digital pathology and image-based analyses (starting
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reliability. · Understanding of hardware accelerators for AI and their operation. · Familiarity with machine learning workloads (e.g., CNNs). As this is a research position, it is necessary
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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PhD, MD/PhD, or equivalent degree in biomedical sciences (e.g., immunology, vascular biology, molecular biology, physiology). Computer Skills N/A Supervisory Responsibilities No Required operation of
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing