Research fellow (University Researcher) in AI for Population Health and Genomics

Tiivistelmä työpaikkailmoituksesta: The University of Helsinki invites applications for a fixed-term (maximum 4 years) Research Fellow (University Researcher) in AI for Population Health and Genomics with start as soon as possible. The role involves developing an independent AI research program on large-scale biomedical data, including foundation models for longitudinal EHRs and genomics, multimodal learning, and causal representation, in collaboration with the Ganna Lab and the Probabilistic Machine Learning Lab. Applicants must hold a doctoral degree with postdoctoral research experience, a strong publication record in top AI or biomedical venues, demonstrated independent research activity, and excellent English. The monthly salary is 4800–6000€ and the position offers access to nationwide longitudinal health records, FinnGen genomic data, multi-omics datasets, and world-class computing infrastructure. Tämä tiivistelmä on luotu tekoälyn avulla. The Faculty of Science and the Finnish Institute for Molecular Medicine Finland (FIMM), HiLIFE at the University of Helsinki invite applications for a joint (50%:50%) position as RESEARCH FELLOW (UNIVERSITY RESEARCHER) IN AI FOR POPULATION HEALTH AND GENOMICS for a fixed-term appointment of maximum 4 years with starting date as soon as possible. POSITION DESCRIPTION The position relates to the Sustainable Wellbeing Across Lifespan (SWAN) ( https://www.swan-helsinkiuni.net/ ) research action that conducts cutting-edge research to promote health span by identifying individual risks and susceptibilities, aiming to clarify mechanisms of degeneration across the lifespan and exploring new approaches to detecting, monitoring and treating remodeled metabolism associated with ageing. The candidate is expected to develop an independent research program within this scope, focusing on methodological innovation and the application of modern AI approaches to large-scale biomedical data. Research topics may include foundation models for longitudinal electronic health records and genomics, multimodal learning integrating health records with molecular data, causal representation learning, in-context learning and amortized inference for healthcare decision-making, and AI-assisted scientific discovery workflows. The work will be done together with the Ganna Lab ( https://www.dsgelab.org/ ) at FIMM and the Probabilistic Machine Learning Lab ( https://www.helsinki.fi/en/researchgroups/probabilistic-machine-learning ; groups of Acerbi and Klami) at the Department of Computer Science, aligning with concurrent projects developing foundation models for healthcare. The groups are active members of the ELLIS Institute Finland ( https://www.ellisinstitute.fi/ ), providing opportunities for collaboration with leading researchers in artificial intelligence across Finland and Europe. The position offers access to globally unique research resources, including nationwide longitudinal health records from Finland covering more than five billion healthcare events, FinnGen with genomic data from over 500,000 participants, and extensive multi-omics datasets including proteomics and single-cell transcriptomics. Researchers also benefit from access to world-class computing infrastructure through CSC and LUMI, one of Europe's most powerful supercomputers. RESPONSIBILITIES The appointed researcher is expected to pursue independent research on methodological innovation in developing and applying AI methods, in collaboration with existing research groups at the university, and will have the opportunity to be part of ELLIS Institute Finland activities. They will also participate in university-wide activities advancing the use of AI in scientific research, together with other researchers working in the intersection of AI and other scientific disciplines. The daily working activities of the appointed researcher will be split between the Faculty of Science and FIMM. Opportunities for contribution to teaching (up to 5-10% of annual working time) will be provided. QUALIFICATIONS We require a doctoral degree and postdoctoral research experience in artificial intelligence, machine learning, or computational statistics, with demonstrated interest in applications to population health and/or human genetics. Excellent communication skills in English are necessary. The candidate needs to have: A doctoral degree in a suitable field Research activity after PhD (typically 2-4 years of postdoctoral research), ideally with evidence of independent research contributions, ability to lead a research group, experience in research funding acquisition, and public engagement. Scientific competence in developing and/or applying artificial intelligence methods with proven publication record in scientific venues in the field. We expect publication record in top-tier venues in AI and machine learning (e.g. NeurIPS, ICML, ICLR, or similar) and/or publications with clear AI contributions in leading biomedical journals (e.g. Nature Genetic