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and have synergiccollaborationeffects. Weexpect a motivatedearlycareer researcher with stronginterest and experience with GIS/earth observation/climateprojection data as well as machine learning models
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State University and work closely with I-CREWS researchers across the state. The position emphasizes developing, integrating, and applying modeling approaches-including machine learning (ML), hydrologic
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working on diverse scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and
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, etc.) Knows-how: • Design, implement, and evaluate machine learning and deep learning models, including multimodal architectures • Process, clean, and integrate heterogeneous datasets (electrical
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. Technical skills: Proficient in using E-views and Stata for econometric modeling. Advanced expertise in business intelligence, data analysis, data science, and machine learning tools. Capable of developing
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 18 days ago
that diffusion models are a fundamental divergence from traditional deep learning paradigms. This suggests that existing generalisation theories are insufficient and highlights the need for a bespoke, algorithm
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(or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs. The candidate will apply
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 17 days ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be