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, cooperative systems, data mining and machine learning, education, didactics and entertainment computing, or Neuroinformatics. Across faculties, renowned researchers in the social sciences, philosophy, and
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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, computer science, or related fields. Demonstrated hands-on experience with machine learning techniques. Strong programming skills (Python preferred). Experience analyzing time-series data, preferably from
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analysis Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within an experimental team, with direct availability of experimental
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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to weather forecasting centres and the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO) Using innovative techniques (data assimilation, machine learning, etc.) for the GNSS
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and exciting research opportunities for scientific inquiry. Overall, the distinct areas of investigation for the Grant and the Sengupta labs allows the ideal candidate to acquire and develop different
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, computer science, data science, engineering, or related field and have research experience or interests at the interface of neuroscience and computation, in particular artificial intelligence and machine learning
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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, CNRS, I3S, Sophia-Antipolis, France) Collaboration: Luca Calatroni (Luca.calatroni@unige.it), Machine learning Genoa Center, Italy. Context and Post-doc objectives Conventional optical microscopy