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the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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(graduated or close to graduation) in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields. Scientific curiosity and creative thinking
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candidate will hold a PhD in geosciences, applied machine learning, data assimilation, or applied mathematics. The selection will be based on the following scientific and technical criteria: Experience in
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perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Transactions on Machine Learning Research, 2024 Kugler, F. Forbes, and S. Douté. Fast
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democratization of approaches using artificial intelligence based on Machine Learning (statistical AI), data lakes have also been proposed [4,5]. Objectives: Monitoring farming and agronomical activities is based