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.

In this context, the Funke lab develops machine learning methods to accelerate scientific discovery with a focus on microscopy image analysis. Specifically, we are interested in the development of new methods to identify structures of interest in large datasets (detection, segmentation, and tracking), the creation of explainable AI methods to interrogate scientific datasets, and the design and implementation of mechanistic models of biological processes that work together with contemporary machine learning methods.

We are looking for an ambitious Postdoc who will lead our efforts on the development of new methods for cross-modality prediction and explainable AI. This project will be both theory-heavy and have concrete applications: using state-of-the-art machine learning methods, we will develop novel probabilistic frameworks for domain translation and apply them to real-life scientific datasets to model nonlinear correlations between different modalities and to produce counterfactual explanations that uncover and visualize those correlations for human understanding.

Your Mission

• Lead the development of novel probabilistic frameworks for cross-modality predictions and explainable AI.
• Guide the development with concrete applications on biomedical datasets in mind.
• Publish your results in conference and journal publications, together with high-quality, open-sourced, and well-documented software tools.
• Initiate collaborations, proactively identify impactful use cases, and mentor junior researchers.

Grow your skills

The Human Technopole is a unique environment at the intersection of many disciplines of the life sciences. In the Funke lab, you will have the opportunity to apply and grow your skills by

• Leading interdisciplinary research projects in collaboration with experimental labs.
• Being closely involved in the experimental design, data generation process, and analysis.
• Writing efficient, reusable, and elegant code that can be used by others to build on top of, or directly by experimentalists for their data analysis needs.
• Mentoring junior researchers and communicating your results amongst peers.

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

What you’ll bring

Essential

• A Ph.D. in Computer Science, Physics, Engineering, Biology, or related fields.
• Experience with machine learning and probabilistic modelling.
• Experience with scientific python packages.
• A track record of impactful scientific contributions (papers, scientific software, high ranking in scientific competitions, or other significant research outputs).

Preferred

• Strong track record of machine learning in the life sciences.
• Experience working with medical and biological image datasets.
• Familiarity with generative modeling (GANs, VAEs, diffusion models, normalizing flows, etc.)
• Experience with explainable AI.

Organizational and Social Skills

• Ability to initiate, lead, and sustain scientific collaborations.
• Excellent communication and mentoring abilities.

At HT, your discoveries contribute to a global effort to improve human health.

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).