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advanced post-training techniques to ensure foundation health models reason accurately over medical knowledge graphs and clinical instructions while verifying factuality and safety boundaries. Agentic
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health records. Medical Reasoning and Grounding: Developing advanced post-training and alignment techniques (RLHF, DPO, GRPO) to ensure foundation health models reason accurately and safely. Agentic
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of the post-doctoral researcher will include • Cleaning and management of health, healthcare and sociodemographic data from multiple nationwide registers • Advanced statistical modelling in large and
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and experimental models. Our research focuses on combining large-scale molecular data - including metabolomics, lipidomics, genomics, proteomics, glycomics, and microbiome data - with advanced
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subject ranking) and is currently investing heavily in life science research (see https://www.helsinki.fi/en/helsinki-institute-of-life-science) . The research team currently consists of 4 post docs, 4 PhD
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. The project combines quantitative modelling of markets and the wider energy system with empirical analysis of how flexibility is created, priced, and traded by market participants. A central objective is to
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will join a research group working on longitudinal models of multiple chronic diseases across the life course. The postdoctoral researcher will contribute to building and evaluating machine learning
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) with clear indication on the motivation to apply for this position, a copy of a PhD degree certificate. If you are interviewed, you will be asked for contact details for two references. The deadline
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. A maximum two-page CV. List of scientific publications. An official copy of the PhD degree certificate. Contact information of 2 references (name, relation to candidate, e-mail and telephone number
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other researchers (MSc and PhD students, post-docs and project researchers), as well as with an extended network of international collaborators. Job description The selected candidate will work on the ERC