Postdoc Funke Group

Human Technopole; Milano; In loco; EUR 39.000 - 47.000; Tempo pieno

Vantaggi offerti da questo lavoro

Relocation support

Flexible working policies

Parental support initiatives

Descrizione del lavoro

Human Technopole in Milan invites applications for a postdoctoral position leading the design, implementation, and training of active learning strategies to fine-tune a pre-trained cell-tracking network. The project focuses on adapting to new datasets with only a few annotated samples and embedding the network in the Motile Tracker GUI.

You will work with contemporary probabilistic models to quantify uncertainty and collaborate with experimental labs, mentoring junior researchers while

Competenze

• PhD or nearing completion in a quantitative field relevant to life sciences.
• Experience with machine learning is essential.
• Experience with scientific Python packages (NumPy, SciPy, scikit-learn, etc.).
• A track record of impactful scientific contributions (papers or software) is desirable.

Mansioni

• Lead the development of an active learning framework for cell tracking.
• Guide development on real biomedical datasets.
• Publish results in conferences and journals with open-source, well-documented software.
• Initiate collaborations and mentor junior researchers.

Conoscenze

Machine learning

Python (scientific)

Formazione

Ph.D. in Computer Science, Physics, Engineering, Biology, or related fields

Strumenti

Python

Motile Tracker

Descrizione del lavoro

Postdoc | Human Technopole, Milan

Application closing date: 10.10.2026

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.

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.

Many life science projects involve the acquisition of time series microscopy data to visualize the dynamics that underline cell proliferation and cell fate decisions. To analyze those datasets and gain novel insights, cells need to be reliably tracked over long periods of time; including the detection of cell division events to create comprehensive lineage trees. Although cell tracking using deep learning methods has been shown to deliver excellent results on selected datasets, experimentalists still struggle to track cells automatically on new datasets: the diversity of possible live-cell datasets makes it hard for a given method to generalize due to differences in fluorescent labeling, resolution, model organism, or exact imaging modality to name a few. No method exists as of now that can reliably handle all of those different conditions.

We are looking for an ambitious Postdoc who will lead our efforts on the design, implementation, and training of active learning strategies to fine-tune a pre-trained cell tracking network. Instead of training a generalist model for all possible cases (which is near impossible), we will instead focus on developing learning methods that can adapt to a new dataset using only very few, carefully selected human annotated samples. We will achieve this by developing an active learning framework, i.e., a deep learning system that actively asks a user for feedback in situations where the method is unsure what the correct answer is. To that end, we will make use of contemporary probabilistic models that are able to communicate their uncertainty about a given decision.

Practically, we will embed this network in the Motile Tracker (https://github.com/funkelab/motile_tracker), an established GUI for cell tracking.

Your mission

• Lead the development of an active learning framework for cell tracking.
• Guide the development on real biomedical datasets.
• 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.
• Mentoring junior researchers and communicating your results amongst peers.

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

Essential

What you'll bring

• A Ph.D. in Computer Science, Physics, Engineering, Biology, or related fields, either completed or nearing completion.
• Experience with machine learning.
• 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 cell tracking.
• Experience working with probabilistic models.
• Experience with active learning.

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.

This is a 4-year contract offered under CCNL Chimico Farmaceutico, 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).