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team of chemists, physicists, mathematicians, data scientists and machine learning experts characterizing PFAS molecules used in the semiconductor industry with the goal of discovering new molecules
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health. The post holder will develop innovative AI techniques approaches applied to oral healthcare, with a particular focus on Large Language Model-based multimodal data understanding and reasoning
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invariant Natural Killer T (iNKT) cells for precision targeting of AML, an aggressive leukaemia with poor survival outcomes. Building on strong preliminary data, it combines mechanistic and translational
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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to conduct data analyses for large-scale multimodal (genomics, omics etc) studies. Conceptualise new ideas, lead data-driven discoveries, ensuring in-depth assessment and interpretation of results. Conduct
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nanofabrication techniques and processes. § Data analysis with MATLAB, Mathematica, Python or similar software. § Strong oral and written communication skills. Application Instructions Please upload
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9 Mar 2026 Job Information Organisation/Company Łukasiewicz PORT Department P4Health Research Field Biological sciences » Biological engineering Researcher Profile Recognised Researcher (R2
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16 Apr 2026 Job Information Organisation/Company UiT The Arctic University of Norway Department Department of Community Medicine Research Field Medical sciences » Other Ethics in health sciences
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Max Planck Institute for Political and Social Science, Göttingen | Gottingen, Niedersachsen | Germany | 24 days ago
expertise; excellent command of quantitative research methods, including panel regression, analysis of large administrative data, survey data, and statistical programming in Stata/R/etc; excellent oral and
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sustained research trajectory in addiction health services research and policy evaluation. Responsibilities Design and execute advanced quantitative analyses using large secondary administrative health data