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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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machine learning models that predict soil health and crop performance. The position will exploit datasets integrating biochemical and molecular soil parameters (with a focus on microbiome features from
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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computer-based systems and the preparation of data for inclusion in lab books, presentations and publications. Maintain a hardcopy or electronic lab book · Work in compliance with relevant Health and Safety
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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or pre-printed at least one primary research paper as first or co-first author You have some experience in experimental work Desirable but not required/ Nice to have A strong foundation in machine learning
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atmospheres and detectability studies Model development of 3D stellar atmospheres Applications of machine learning and AI to exoplanet data analysis Biomarkers and habitability of Earth-like planets Where
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, transcriptomics, proteomics), machine learning, statistical analysis and programming languages such as R or Python. - Experience in image analysis, including development of custom ImageJ plugins and workflows