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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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the robot's physical embodiment suffer from poor generalization, weak explainability, and limited transferability; (ii) sample-inefficient learning requires large volumes of annotated, domain-specific data; and
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theorists. The overview of KMI and members can be found at http://www.kmi.nagoya-u.ac.jp/ ; Requirements: A PhD (or close to completion) in physics or related disciplines, such as astronomy, computer science
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at: www.fz-juelich.de/gp/Careers_Docs Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd We
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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community! The position This position will develop and maintain data-processing pipelines for large-scale neuronal and behavioral datasets, including multi-day high-frequency recordings, fluorescence
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research profile, and an international network around big data in marine sciences. The candidate will have access to NIOZ’s high-performance computing cluster, GPU nodes for deep learning, dedicated data
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23 Dec 2025 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country United Arab
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and scope of MS research. This PhD project employs advanced network analysis and Large Language Models to develop predictive models for MS progression. It involves constructing and analyzing a complex
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tasks: You will work together with renowned astrophysicists and computer scientists in the DFG-funded “Dynaverse” Excellence Cluster You will invent, implement, and benchmark novel AI tools (reinforcement