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Universitätsklinikum Heidelberg – UK Mannheim GmbH | Mannheim, Baden W rttemberg | Germany | 7 days ago
and machine learning methods applied to biological data Experience working with multiomics data integration is a plus Excellent organizatorial skills, strong commitment, motivation and ability to work
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! Education PhD in Computer Science with a focus on AI and/or cybersecurity Experience and skills 1-2 years of post-PhD research and engineering experience in AI for cybersecurity Strong background in large
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Computer Science, Statistics, Finance, Economics, or a related field. Very strong programming skills, especially in Python. Hands-on experience with large language models, AI agents, or machine-learning systems
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research. Experience with large datasets, longitudinal studies, or population health research. Expertise in advanced epidemiologic methods (e.g., causal inference, biostatistics, machine learning
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 18 days ago
and fine-grained semantic information within the prompts, and assess geometric accuracy of corresponding models' answers. If necessary, we will then propose dedicated learning strategies for inducing
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and social data. Minimum Requirements: A PhD degree is required. Preferred Qualifications: Experience with large-scale or high-dimensional datasets (e.g., cohort, registry, or omics data) Familiarity
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, and registry-linked outcome data. In this project, you will develop and apply AI-based methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen
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meters. These instruments produce large amounts of data that require several processing steps before the relevant physical variables are obtained. Typically, machine learning methods are used to optimize
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of ion conductivity in complex battery materials on a large scale. Model‑generated data will be used to identify key relationships between material structure and ionic conductivity through advanced data