Staff Scientist

Build the science that shapes the future of human health.
Final date to receive applications: 14.01.2026

Join a place where ambitious science thrives
Human Technopole (Milan) is a rapidly expanding life science institute where international researchers and cutting‑edge technologies converge to accelerate biomedical discovery. Our mission is to transform bold scientific ideas into advances that improve human health.

Within this mission, the Health Data Science Centre (HDS Centre) at HT has been planned in partnership with Politecnico di Milano and is developing several lines of research, with the aim to help deliver a step‑change in health data science in Italy. The Centre’s mission is to systematically generate, mobilise, and harvest “big data” allowing agnostic and dynamic collection of information, to deliver a new class of research that will enable a better understanding of the clinical, molecular, behavioural and environmental determinants of non‑communicable diseases, for both patients and public benefit.

We are seeking an ambitious Staff Scientist toto support the activities of the Di Angelantonio/Ieva Research Group in the HDS Centre.

Your mission
As Staff Scientist you will play a central role in delivering impactful research outcomes and in building sustainable research infrastructure with reach beyond the group and institute. You will help the two PIs in the group define the direction of the scientific work, conduct research, analyse data, write scientific manuscripts for publication and disseminate results. Your work is expected to lead to high‑impact publications.

Moreover, you will be in charge of jointly supervising the PhD, internship and masters’ students in the group.

Grow your skills
You will enhance your scientific and professional skills by:

Designing and leading epidemiological and statistical analyses to address clinically and biologically relevant questions using molecular and multi‑omics data (e.g. genomics, proteomics, metabolomics) within large population‑based and clinical cohorts;

Working closely with colleagues to help interpret findings and draft manuscripts and other reports for publication;

Collaborating with statisticians and other colleagues to help develop and apply methodologically suitable analysis strategies to planned investigations;

Jointly with the two PIs in the group, scientifically supervising PhD and masters’ students and overseeing post‑doctoral research activities;

Proposing projects and obtaining further funding by applying for grants that are relevant to the group’s research lines;

Anticipating, communicating and solving any potential problems that might arise when doing research;

Contributing to reports, presentations and publications;

Helping to get under way new projects and collaborative consortia in the
-omics research field in general, and in genomics in particular;

Reviewing, analysing or presenting the results of your own research and of that of the junior researchers you will be jointly supervising;

Supporting the two PIs in the group in a range of key activities that would bring visibility to the centre and that align well with HT broader goals, such as organising and running training courses aimed at sharing the expertise available within the HDS Centre with the rest of HT and the Italian scientific community;

Contributing to the development of institutional strategies.

Human Technopole supports career development through training, mentoring and dedicated learning opportunities.

What you’ll bring
Essential

A relevant PhD degree (e.g. Epidemiology, Biostatistics, Statistics, Genetics, Data Science, Mathematics);

Strong understanding of epidemiological methods and statistical modelling, including methods relevant to molecular and genetic data;

Experience analysing and integrating multi‑omics data and population‑based health data (e.g. cohort studies, biobanks, electronic health records);

A sound understanding of Statistical and Machine Learning concepts, particularly in relation to genomics;

Prior experience working with multiple data sources and modalities, and a sound understanding of data integration methodologies;

Experience with using statistical or other…