Application Closing Date: October 9th, 2025
Job Overview
The Human Technopole (HT) is a pioneering interdisciplinary life science research institute supported by the Italian Government. Its mission is to cultivate innovative strategies for enhancing human health. HT is structured into five collaborative Centres: Health Data Science, Genomics, Computational Biology, Neurogenomics, and Structural Biology, all aimed at fostering open and interdisciplinary research to advance life science initiatives both in Italy and beyond.
Focus on Health Data Science
At the heart of HT lies the Health Data Science Centre (HDSC), which is dedicated to the systematic generation, mobilization, and harvesting of "big data." This Centre is committed to creating a dynamic collection of information to better understand the clinical, molecular, behavioral, and environmental factors driving non-communicable diseases. Through rich genetic, molecular, and diverse omics data resources, the HDSC aims to provide significant benefits to patients and society while promoting a comprehensive understanding of human health.
Postdoctoral Opportunities
The Di Angelantonio-Ieva group at HDSC is currently recruiting up to two Postdoctoral Researchers in the field of Generative Machine Learning for Biomedical Data. This role presents an exciting opportunity for researchers to engage with cutting-edge generative models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and transformer-based architectures, specifically designed for large-scale biomedical datasets. The candidates will work on a variety of data types, including genomic profiles and clinical event sequences.
The postholders will focus on developing advanced modeling techniques for prediction and simulation, aimed at understanding disease trajectories and outcomes. Collaboration will be at the core of this role, as the researchers will work alongside geneticists, molecular epidemiologists, and other data scientists to further the application of precision medicine.
Key Responsibilities
The successful candidates will undertake a range of tasks, including:
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Designing Analyses: Lead studies using state-of-the-art generative models on large biomedical datasets, focusing on predicting disease progression and crafting realistic patient profiles.
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Building Pipelines: Develop and optimize data integration pipelines for merging clinical, genomic, and other biological data sources into unified representations for analysis.
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Model Development: Create generative models that simulate patient health trajectories based on real-world datasets, thus enhancing the predictive accuracy for precision medicine applications.
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Cross-Disciplinary Collaboration: Coordinate with various experts in the Centre to implement robust frameworks for predicting disease evolution.
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Effective Communication: Interpret and share findings through high-quality publications and presentations.
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Research Governance: Ensure that all research adheres to ethical standards while promoting open science practices.
- Training and Outreach: Engage in mentoring roles by supporting MSc and PhD students and participating in public outreach activities.
Essential Job Requirements
Candidates interested in this position should possess the following qualifications:
- A PhD in a relevant field such as computer science, data science, or mathematics.
- Proficiency in generative models and their application to biomedical data.
- Experience with machine learning frameworks and programming languages, particularly Python.
- Strong quantitative skills, with a background in observational or clinical studies.
- A proven track record of scientific publications centered on machine learning applications.
Proficiency in English is crucial, while additional language skills and prior experience in predictive modeling of disease trajectories are advantageous.
Preferred Skills and Attributes
Ideal candidates will also demonstrate:
- Previous experience in applying various generative machine learning methods to biomedical, clinical, or genomic datasets.
- Familiarity with methods for disease trajectory prediction.
Candidates should be keen on working accurately with meticulous attention to detail, possess strong collaboration abilities, and be self-motivated.
Working at Human Technopole
HT provides a supportive and inclusive work environment focused on scientific excellence and teamwork. The organization believes that diverse teams generate innovative solutions that drive research forward. Additionally, HT promotes training opportunities, including workshops and conferences, to foster the continuous development of its scientists.
Benefits of Employment
Working at HT comes with numerous advantages:
- Comprehensive welfare plans and access to a canteen service.
- Measures to ensure a healthy work-life balance.
- Italian language training for expatriates and parental leave provisions.
- Attractive income tax benefits for researchers relocating to Italy.
Application Process
To apply, interested candidates should submit:
- A detailed CV.
- A motivation letter in English that aligns their skills and experience with the job requirements.
- The contact details of three referees as part of the CV.
The application can be submitted through the LinkedIn platform.
Contact Information
For further inquiries regarding the role, candidates may reach out to recruitment@fht.org (please note this email is not for submitting applications).
Whether you are an experienced researcher or an enthusiastic newcomer in the field of generative machine learning, this opportunity at Human Technopole offers a chance to make impactful contributions to biomedical research while working in an inspiring and collaborative environment.