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understanding and generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research
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-scale text and structured biomedical data; Strong quantitative and analytical skills applied to observational or clinical datasets, and familiarity with techniques for representation learning and sequence
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of strictly anoxic systems Microbial physiology assays, analytical chemistry and metabolomics Meta-omics approaches Fluorescence in situ hybridization (FISH, CARD-FISH) & advanced microscopy. Candidates with
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biophysics, computational biology, mathematics in the life sciences, computer science and machine learning with application to biological systems, and related areas. What we provide The CSBD provides fully
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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Technopole supports career development through training, mentoring and dedicated learning opportunities. What you'll bring Essential PhD Degree in a relevant scientific field (e.g. genetic epidemiology
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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to work on the discovery of new superconducting materials with high critical temperatures, using novel methods and concepts such as machine learning and quantum geometry. The project is related to large
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leading academic and industry partners within the Horizion Europe Project. Your profile We are looking for a highly motivated and talented candidate with a background in deep learning. The required
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Computer Science, with specialization on applied machine learning, statistical methods, and/or autonomous systems Strong programming skills Strong analytical skills Industry experience in information and