Postdoc in Genetic Epidemiology​/Statistical Genetics

Final date to receive applications:
January 12th 2026
Human Technopole (HT) is an interdisciplinary life science research institute, created and supported by the Italian Government, with the aim of developing innovative strategies to improve human health. HT is composed of five Centers:
Health Data Science, Genomics, Computational Biology, Neurogenomics and Structural Biology. The Centers work together to enable interdisciplinary research and to create an open, collaborative environment to help promote life science research both nationally and internationally.
The Health Data Science Centre focuses on systematically generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical, molecular, behavioural and environmental determinants of non-communicable diseases, ultimately benefiting patients and society. The Centre owns exceptionally rich genetic and molecular (especially proteomic data) datasets in cohorts of various ethnicities.
The Di Angelantonio-Ieva group is seeking to recruit a highly motivated Postdoc in Genetic Epidemiology/Statistical Genetics. The postholder will be involved in or lead major projects to understand the genetic architecture of molecular traits alone and in interaction with the environment. S(h) e will help in defining the direction of scientific work, conducting research, analyzing data, preparing the work for publication and disseminating results.

The work of the post-holder is expected to lead to high-impact publications.
Your main tasks and responsibilities:

Designing and leading analyses to integrate genetic and molecular data in various populations, taking advantage of the exceptionally rich data available at the Centre;
Developing effective ways of handling, cleaning and integrating complex genetic and molecular phenotype data into epidemiological datasets (pipelines);
Working with epidemiologists and other colleagues in the Centre to help develop and implement methodologically appropriate analysis strategies for genetic and ‘omic data integrated into epidemiological investigations;
Collaborating with external (including many international) researchers to lead or contribute to large-scale consortia and meta-analyses;
Working closely with colleagues to help interpret findings and draft manuscripts and other reports for publication;
Anticipating, communicating and solving any potential problems that arise with analyses or other aspects of research projects;
Contributing to reports, presentations and publications by preparing numerical and graphical summaries (visualizations) using relevant computer software;
Assisting with preparing grant applications to secure further funding for relevant projects;
Helping establish new projects and research consortia relating to cardio-metabolic health, including designing and conducting pilot studies;
Following and promoting open science practices within the Centre;
Reviewing, analyzing or presenting information relative to own or related projects when requested;
Contributing to the Centre’s training program by teaching and tutoring on internal and external workshops (online and in person);
Contributing to disseminating scientific results to the public and taking part in patients and public involvement and engagement activities;
Contributing to MSc and PhD students supervision, as requested and appropriate.

Requirements
Essential Requirements

PhD Degree in aology, molecular epidemiology, statistical genetics, biostatistics, health data science, etc) by the time of application or the position starting date;
A sound understanding of applying relevant statistical methods to highly-dimensional complex data from molecular phenotypes coupled with genotypes, for discovery, prediction and causal inference in epidemiological studies (including but not limited to genome-wide association studies of molecular phenotypes, Mendelian Randomization, co-localisation);

Experience of statistical or other programming languages to manipulate large-scale datasets – e.g. Python, R;
Strong quantitative skills and analytical reasoning…