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. Combining satellite, airborne and ground-based measurements with modelling and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research
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data at an internationally competitive level. Experience of biostatistics or machine learning approaches Proficiency in a scripting language like R or Python, as well as ability to work efficiently in a
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between biology and artificial intelligence. Strong collaborative skills, analytical ability, and the capacity to work independently. Merits: Education or training in computer vision, machine learning, deep
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data at an internationally competitive level. Experience of biostatistics or machine learning approaches Proficiency in a scripting language like R or Python, as well as ability to work efficiently in a
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). For more information: https://www.cordis.europa.eu/project/id/101225380 Research focus The PhD candidate will work on one or more of the following interconnected areas: AI‑ and machine‑learning‑based
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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datasets – though all parts will be under supervision and in collaboration with other team members. You will also develop and apply machine learning-based classifiers for cell-type identification, and
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organismal fitness, using MOO techniques, machine learning and genome-wide association studies. Yeast and bacteria are your primary models, but the analytical framework you develop will be broadly applicable
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of experience in training, evaluating, and deploying machine learning models, including deep neural networks and relevant frameworks - Documented several years of experience in systems development with Python and
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candidate will collaborate with various research groups, expand professional network and acquire advanced biochemical and computational skills. This project builds upon our team’s established expertise