Postdoc in Generative Machine Learning for Biomedical Data| Human Technopole, Milan

Build the science that shapes the future of human health.

Application closing date: 21.02.2026

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 Postdoc in Generative Machine Learning for Biomedical Data to support the activities of the Di Angelantonio-Ieva group in the HDS Centre.

Your mission 
As Postdoc you will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures) to large-scale biomedical datasets. These models will be used to work with different types of data from the healthcare and biological domains, including genomic profiles, and clinical event sequences. You will develop advanced modeling techniques to create privacy preserving realistic data, and predict disease trajectories, outcomes, and other clinically relevant endpoints. Moreover, you will also explore and exploit latent spaces to discover meaningful patterns and unlock new insights from complex biomedical data.
Furthermore, you will work in a multidisciplinary environment collaborating with geneticists, molecular epidemiologists, and other data scientists to advance precision medicine research. Your work is expected to lead to high-impact publications.

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

• Designing and leading analyses that apply state-of-the-art generative machine learning models (e.g., VAEs, GANs, transformer-based models) to large-scale biomedical and biological data, including developing and optimizing models to predict disease progression and create realistic patient profiles;

• Building and optimizing pipelines for pre-processing and integrating biological data sources (clinical event sequences, genomic sequences, disease codes) into unified patient representations and state sequences for predicting disease progression and outcomes;
• Developing advanced generative models to simulate patient health trajectories, including disease progression, based on real-world data, enhancing predictive modeling and allowing for scenario testing in precision medicine;
• Applying transformer-based architectures to model sequences of clinical events and other time-ordered data to predict the future course of diseases;
• Collaborating with epidemiologists, geneticists, and other colleagues in the Centre to develop and implement robust machine learning frameworks and pipelines focused on disease evolution prediction;
• Interpreting results and communicating findings effectively through manuscripts, presentations, and reports, contributing to high-impact publications;
• Anticipating, communicating, and solving potential challenges in data integration, model performance, and interpretation;
• Preparing numerical and graphical summaries (visualizations) using relevant software and libraries for dissemination to both scientific and broader audiences;
• Assisting with grant applications to secure further funding for related research initiatives;
• Helping to establish new projects related to generative disease trajectory modeling, synthetic genotypes and patient stratification, including designing and conducting pilot studies;
• Ensuring adherence to research governance and ethical standards and promoting open science practices throughout;
• Reviewing, analyzing, or presenting information related to ongoing and related projects as needed;
• Contributing to the Centre’s training program by teaching workshops or tutorials in machine learning, generative models, or data science methods;
• Engaging with public outreach activities and supporting MSc and PhD students’ supervision as requested.

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

What you'll bring

Essential

• PhD Degree in a relevant scientific field (e.g. computer science, data science, mathematics, engineering, or related) or to be obtained within the next 6 months;
• Strong understanding of generative models (e.g., VAEs, GANs, transformer-based models), including experience with their application to biomedical and biological data;
• Experience with machine learning frameworks and programming languages (e.g. Python) for handling large-scale text and structured biomedical data;
• Strong quantitative and analytical skills applied to observational or clinical datasets, and familiarity with techniques for representation learning and sequence modeling;
• A track record of authoring scientific publications, with a focus on machine learning methods and applications;
• Fluency in English.

Preferred

• Previous experience in applying generative machine learning methods (e.g., VAEs, GANs, transformers) to biomedical, clinical, or genomic data;
• Experience in disease trajectory prediction and generating realistic patient profiles for health-related research.

Organizational and social skills

• Ability to work accurately, attention to detail;
• Self-motivated, able to work independently and manage own workload;
• Excellent communication skills and ability to collaborate across disciplines.
• Enthusiastic team player with strong interpersonal skills.

Why Human Technopole
HT offers an international and dynamic workplace, competitive welfare provisions, flexible working policies and relocation support. Researchers moving to Italy may benefit from attractive tax benefits. We promote work–life balance and provide parental support initiatives.

How to apply
Submit:

• CV.
• Motivation letter (English).
• Contact details of two referees.

This is a 4-year contract offered under CCNL Chimico Farmaceutico, Employee Level B2

Salary: up to € 43.000,00 depending on the candidate’ seniority.

The position is based in Milan, Italy, within our vibrant international campus.

We strongly encourage applications from candidates belonging to protected categories (L. 68/99).