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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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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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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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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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analyse large-scale longitudinal and routinely collected health datasets linked to high-resolution environmental data to quantify associations between heat and cold waves and adverse health outcomes, and to
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research clusters including Energy Transition, Surface Technology and Metallurgy, Polymers and Composites, and Sustainable Materials. With some 100 researchers and PhD students and several national and
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digital transformation. Data sharing between maritime stakeholders is a key enabler for accelerating the operationalization of AI across maritime value chains. This PhD project therefore calls for research
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Information (QG&QI), Quantum Materials (QMat), and Soft Matter (SM). You will work in the Strontium Quantum Gases Group , part of the QG&QI cluster and headed by Prof. Dr. Florian Schreck. We exploit ultracold
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Your Job: In the CrowdING project, you will analyze experimental data from large crowds and develop quantitative measures to describe their spatial structure. To do this, you will use and expand
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is expected to become a full member of the team by SPHEREx first major release, participating actively in the mission’s core science programs. More information about SPHEREx can be found at https