Postdoctoral researcher in Machine Learning and Dynamical Systems

IIT; Genova; Hybrid; EUR 38,000 - 48,000; Full time

Benefits offered by this job

Private healthcare coverage

Job summary

IIT in Genoa invites applications for a Postdoctoral researcher in Machine Learning and Dynamical Systems, based in the Computational Statistics and Machine Learning Unit. The project leverages the ERC Advanced Grant LEO to learn evolution operators from trajectories.

You will work at the intersection of ML, applied math, and dynamical systems, contributing to theory, algorithms, and applications, with opportunities for collaboration and mentoring.

Qualifications

• PhD in applied math, CS, physics, or related field.
• Strong publication record in ML / dynamical systems.
• Strong programming skills in Python and ML tools.
• Good English, able to publish and communicate results.

Responsibilities

• Investigate open problems in ML and data-driven dynamical systems.
• Develop theoretical and computational methods.
• Publish and present research findings.
• Contribute to open-source software supporting reproducibility and dissemination.
• Mentor PhD students and interns.

Skills

Python programming

TensorFlow

Machine learning theory

Dynamical systems

Education

PhD in applied mathematics, CS, physics, or related

Tools

SciPy

TensorFlow

Job description

Postdoctoral researcher in Machine Learning and Dynamical Systems

Contract and commitment: collaboration contract, for up to three years

Location: CHT Erzelli, Genoa, Italy

Step into a world of endless possibilities, together let's leave something for the future!

At IIT, we are committed to advancing human-centered Science and Technology to address the most urgent societal challenges of our era. We foster excellence in both fundamental and applied research, spanning fields such as neuroscience and cognition, humanoid technologies and robotics, artificial intelligence, nanotechnology, and material sciences, offering a truly interdisciplinary scientific experience. Our approach integrates cutting-edge tools and technology, empowering researchers to push the limits of knowledge and innovation. With us, your curiosity will know no bounds.

We are dedicated to providing equal employment opportunities and fostering diversity in all its forms, creating an inclusive environment. We value the unique experiences, knowledge, backgrounds, cultures, and perspectives of our people. By embracing diversity, we believe science can achieve its fullest potential.

THE ROLE

This position is based in the Computational Statistics and Machine Learning Unit, led by Massimiliano Pontil, and is funded by the ERC Advanced Grant project "LEO - Learning Evolution Operators" (Grant Agreement ID: 101267213). LEO studies the problem of learning the dynamics underlying temporal phenomena from observed trajectories.

Linear evolution operators play a central role, providing a powerful perspective for studying complex nonlinear systems through linear representations in suitable function spaces. Their spectra encode important physical properties of these systems, and a key objective of the project is to develop methods for learning these operators and estimating their spectra.

With a strong focus on the foundations of machine learning, LEO aims to develop efficient and reliable algorithms that advance the state of the art. To this end, the project combines interactive learning, the learning and transfer of shared representations across multiple systems, and the incorporation of first-principles knowledge (i.e., information about the underlying governing equations). The learned operators will be used to infer interpretable global properties of complex systems, including critical transitions in atomistic systems modeled by the Langevin equation and regime shifts in climate systems modeled by partial differential equations.

Successful candidates will work at the intersection of machine learning, applied mathematics, and dynamical systems, with opportunities to collaborate with domain experts in scientific applications. Depending on their background and interests, candidates may contribute to theoretical foundations, algorithm development, applications, or a combination of these areas.

RESPONSIBILITIES

• investigate open problems in machine learning, data-driven dynamical systems and scientific applications;
• develop new theoretical and computational methods;
• publish and present high-quality research;
• contribute, where appropriate, to open-source software supporting reproducibility and dissemination;
• possibly contribute to mentoring PhD students and interns.

ESSENTIAL REQUIREMENTS

• A PhD in applied mathematics, computer science, physics, or a closely related discipline;
• Strong expertise in at least one of the following areas: computational linear algebra, dynamical systems, functional analysis and operator theory, machine learning theory, probability and statistics, partial differential equations, stochastic differential equations;
• Strong publication record in leading conferences and/or journals relevant to the position;
• Strong Programming skills (Python, TensorFlow, and related tools for scientific computing and machine learning);
• A good command of spoken and written English;
• The ability to communicate, organise and publish research findings effectively;
• The ability to work both independently and collaboratively in a highly interdisciplinary environment.

COMPENSATION PACKAGE

• A gross annual salary of between €37,500 and €48,000
• Private healthcare coverage may be available, depending on the role and contract;
• Candidates from abroad or Italian citizens who have carried scientific research activity permanently abroad and meet specific requirements, may be entitled to a deduction from taxable income of up to 90% from 6 to 13 years.

Please submit your application through the online application form by 31 October 2026. Applications should include:

• a curriculum vitae;
• a research statement of no more than three pages describing your previous work and research interests relevant to the position;
• two reference letters.

Application deadline: 31 October 2026

To discover more about life at IIT, visit the dedicated section: https://www.iit.it/en/work-at-iit