Human Technopole
2 months ago
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Postdoc in Statistical Genetics at Human Technopole Human Technopole in Italy
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Sep 7, 2026
Country
Italy
University
Human Technopole

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About this position
Postdoc in Statistical Genetics at Human Technopole, Milan, Italy. The Casale Lab is launching at Human Technopole and is building AI methods that transform population-scale human data into mechanistic models of disease. This postdoctoral role sits at the intersection of statistical genetics, machine learning, and mechanistic biology, with a strong emphasis on connecting human genetic variation to biological function.
The successful candidate will work with population-scale human genetics alongside single-cell, spatial, and multimodal molecular data to identify the cells, cellular states, and biological programs through which genetic variation influences disease. The lab highlights opportunities to develop next-generation methods, drive biological discovery across molecular and clinical datasets, and build an internationally recognized research profile through publications and conferences.
Eligibility highlights: a PhD in Statistical Genetics, Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related quantitative discipline; a strong publication record in computational biology/genetics/biomedical journals; excellent Python programming and research software development experience; and strong knowledge of modern AI, including deep learning and representation learning. Preferred experience includes GWAS, biobanks, population-scale genetics, single-cell or spatial transcriptomics, functional genomics, Bayesian statistics, causal inference, probabilistic modelling, and foundation models.
Location: Human Technopole, Milan (Italy). Formal recruitment opens: September 7, 2026. Applications will be submitted through the official Human Technopole recruitment portal and require a CV, motivation letter, and two referees. Informal enquiries are welcomed in advance via [email protected].
Funding details
Available
What's required
Applicants must have a PhD in Statistical Genetics, Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related quantitative discipline. A strong publication record in leading computational biology, genetics, or biomedical journals is expected, ideally with work on computational methods motivated by biological questions using population-scale human genetics or multimodal molecular data. Essential skills include excellent Python programming and experience developing research software for large-scale genomic or molecular datasets, plus strong understanding of modern machine learning and AI, including deep learning and representation learning. Preferred experience includes GWAS, biobanks, population-scale genetics, single-cell or spatial transcriptomics, functional genomics, Bayesian statistics, causal inference, probabilistic modelling, and modern AI architectures/foundation models. Strong analytical thinking, independence, communication, scientific writing, creativity, and collaborative ability are also emphasized.
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