Postdoc In Generative Machine Learning For Biomedical Data

APPLICATION CLOSING DATE:
October 9th 2025

Job description

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 Centres:
Health Data Science, Genomics, Computational Biology, Neurogenomics and Structural Biology. The Centres 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 (HDSC) focuses on systematically generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information enabling 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 Our data resources include exceptionally rich cohorts with diverse omics, imaging, and molecular measurements.

The Di Angelantonio-Ieva group is seeking to recruit up to two highly motivated Postdocs in Generative Machine Learning for Biomedical Data . The postholders 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. The postholders will develop advanced modeling techniques to create privacy preserving realistic data, and predict disease trajectories, outcomes, and other clinically relevant endpoints. They will also explore and exploit latent spaces to discover meaningful patterns and unlock new insights from complex biomedical data.

The postholders will work in a multidisciplinary environment collaborating with geneticists, molecular epidemiologists, and other data scientists to advance precision medicine research.

Your main tasks and responsibilities:

• 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 tothe 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.

Job requirements

Essential Requirements

• PhD Degree in a relevant scientific field (e.G. computer science, data science, mathematics, engineering, or related);
  
• Strong understanding of generative models (e.G., VAEs, GANs, transformer-based models), including experience with their application to biomedical and biological data;
  
• Experience withmachine 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 Requirements

• 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.

Any questions concerning the role and/or queries regarding the relevant terms and conditions should be addressed to (this email address should not be used to send applications).

Application instructions

Please apply by sending:

• Your CV;
  
• A motivation letter in English highlighting how your methodological skills, scientific knowledge, and work experience fit with the position’s specific requirements (N.B. please send your CV and motivation letter as a single document);
  
• The names and contact details of 3 referees.

WHY HUMAN TECHNOPOLE

HT seeks scientific excellence. We recruit the best scientific talents through international, open calls.

Our working environment is international, friendly and inclusive. Our scientists work together across disciplines on research topics of biomedical relevance, leveraging synergies between their diverse skill sets and methodological approaches.

We believe that highly diverse teams yield the best and most innovative results.

We engage in outward-facing scientific activities aimed at benefiting the national and international research community. Training is also at the heart of our activity, with initiatives and opportunities for our staff, including scientific courses, conferences and workshops.

MAIN BENEFITS

• Welfare plans;
  
• Canteen service;
  
• Work-life balance provisions;
  
• Italian language training for foreigners;
  
• Parental leave up to 1 year and other support for new parents;
  
• Counselling;
  
• Possibility of flexible working hours;
  
• Support for relocation;
  
• Researchers coming to Italy for the first time, or returning after residing abroad, benefit from very attractive income tax benefits.

Special consideration will be given to candidates who are part of the protected categories list, according to L. 68/99

Number of positions offered:
2

Contract offered:
CCNL Chimico Farmaceutico, Fixed-term 3 years in first instance with option for extension - employee level.

Salary range:
€ 40k - € 50k based on seniority and previous experience.

Location:
in Milan

“The Foundation reserves the right, at its sole discretion, to extend, suspend, modify, revoke, or cancel this job posting without giving rise to any rights or claims whatsoever in favour of the candidates;
the Foundation reserves, however, the right not to proceed with the awarding of the above-described assignment due to the effect of supervening regulatory provisions and/or obstructive circumstances”.