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Arto Klami

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6 days ago

Research Fellow (University Researcher) in AI for Population Health and Genomics University of Helsinki in Finland

Degree Level

Postdoc

Field of study

Computer Science

Funding

Fixed-term joint 50%:50% research fellow position for up to 4 years with a monthly salary of 4800-6000€ depending on experience and qualifications.

Deadline

Nov 16, 2026

Country flag

Country

Finland

University

University of Helsinki

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Keywords

Computer Science
Biology
Artificial Intelligence
Medical Science
Population Health
Salud Pública
Genetic
Genomic
Computational Statistics
Scientific Discovery
Statistics
Electronic Health Record
ML

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About this position

Research Fellow (University Researcher) in AI for Population Health and Genomics at the University of Helsinki, jointly hosted by the Faculty of Science and the Finnish Institute for Molecular Medicine Finland (FIMM), HiLIFE.

This fixed-term research appointment sits at the intersection of artificial intelligence, human genetics, and population health. The successful candidate will develop an independent research program within the Sustainable Wellbeing Across Lifespan (SWAN) research action, with a focus on methodological innovation and the application of modern AI approaches to large-scale biomedical data.

The research environment connects several active groups and infrastructures. The work will be carried out with the Ganna Lab at FIMM and the Probabilistic Machine Learning Lab at the Department of Computer Science, alongside concurrent efforts on foundation models for healthcare. The position is also tied to the ELLIS Institute Finland, creating opportunities to collaborate with researchers in AI across Finland and Europe.

The role gives access to uniquely rich data resources, including nationwide longitudinal health records from Finland, FinnGen genomic data, and extensive multi-omics datasets such as proteomics and single-cell transcriptomics. The researcher will also have access to CSC computing resources and the LUMI supercomputer, supporting ambitious AI and biomedical data science projects.

The appointee is expected to contribute independently to AI method development and application while engaging with existing research groups and university-wide activities at the intersection of AI and other scientific disciplines. The position is split between the Faculty of Science and FIMM, and includes limited opportunities to contribute to teaching.

Professor Arto Klami and Professor Andrea Ganna are listed as contacts for further information on the project and the position.

Funding details

Fixed-term joint 50%:50% research fellow position for up to 4 years with a monthly salary of 4800-6000€ depending on experience and qualifications.

What's required

A doctoral degree is required, together with postdoctoral research experience in artificial intelligence, machine learning, or computational statistics, typically 2-4 years after the PhD. Applicants should show an independent research track record, ability to lead a research group, experience in research funding acquisition and public engagement, strong scientific competence in developing and/or applying AI methods, and a publication record in top-tier AI/ML venues such as NeurIPS, ICML, or ICLR, or in leading biomedical journals with clear AI contributions. The research interest should align with population health and/or human genetics and connect to existing University of Helsinki research groups.

How to apply

Submit the application through the University of Helsinki Recruitment System using the Apply now link. Include a CV, a list of publications, and a 2-5 page document describing your qualifications for the Research Fellow level and your planned research. If invited to interview, provide contact details for two referees.

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Professors

Professor at University of Helsinki

University Name
Country flag

Finland

Research Interests

Artificial Intelligence

Medical Imaging

Remote Sensing

Ml

Spectral Imaging

Professor at University of Helsinki

University Name
Country flag

Finland

Research Interests

Biostatistics

Epidemiology

Medical Science

Genetic

Population Health