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, entomology and machine learning. This position will contribute to One Health epidemiology research projects on the impacts of environmental change on vector-borne and zoonotic disease risks. This work will
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, including rigorous experimental design, model evaluation, and scientific publication. Expertise in at least one relevant area, such as time-series modeling, computer vision, representation learning, NLP
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, network-based risk-sharing models, and the application of statistical and machine-learning methods to evaluate and predict risk reduction. The position may also involve preparing figures, tables, and
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: Bachelor’s degree in Computer Science, Software Engineering, or a related technical discipline. AI & Machine Learning: Strong background in AI model training, computer vision, image processing, and
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. Candidates should have: • A demonstrated record of original research in NLP, machine learning, or a related area • Experience conducting empirical research with large language models • Strong programming and
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and Ni and with energy densities exceeding LMFP and competitive with NMC. A postdoctoral research position is now available on this 3D-CAT project in the area of computer modelling and materials design
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 15 days ago
and physical oceanography, data assimilation and machine learning. Gathering this large panel of skills, the team aims at improving our understanding, reconstruction and forecasting of ocean dynamics
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. The position combines Empa UESL’s expertise in developing and accessing energy system models with the methodological expertise of the IMOS Laboratory in machine learning and foundation models. Postdoctoral
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Computer vision for Physical AI Vision-language(-action) and visual foundation models, multimodal learning Generative AI methods for the aforementioned fields The successful candidate will lead a new
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Summary Support an industry-sponsored research project, funded by a Honda AI+X Seed Grant, that develops an explainable, cost-sensitive machine-learning framework to predict equipment component