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dysbiosis drives immune dysregulation and disease progression in pediatric patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop and train deep learning models (CNNs, ...) data to predict IPLSs
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning and particle physics. Project description Searches for dark
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physics Engineering » Electronic engineering Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 30 Nov 2026 - 16:06 (UTC) Country France Type of Contract
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processes, the foundations of deep learning. Experience coding with deep learning libraries such as Pytorch/JAX is essential. Fluent written and spoken English skills as well as contributions to the group
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution