Postdoc position in RNA Technologies Flagship – Foundation Models RNA Regulatory Networks

Position: Postdoc position in RNA Technologies Flagship – Foundation Models for RNA Regulatory Networks: [...]
Postdoc position in RNA Technologies Flagship – Foundation Models for RNA Regulatory Networks: from RNA-RNA Interactions to RNA-DNA Genome Regulation
Italy

Genoa

Contract:

postdoc (2 years, renewable)

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
Within the RNA Technologies Flagship Fondazione Istituto Italiano di Tecnologia (IIT) is seeking a highly motivated postdoc with a strong background in applying machine learning and deep learning techniques to the biological sciences. The ideal candidate will have expertise in artificial intelligence, with a specific focus on deep learning applications in structural biology. This is a unique opportunity to join a dynamic, interdisciplinary team operating at the intersection of computational genomics, RNA biology, and precision medicine.

The goal is to build a strategic institutional resource for advancing discoveries in cancer, neurodevelopmental, and neurodegenerative disorders, through the integration of computational modeling of RNA and understanding RNA interactions.

The project aims to develop a new generation of artificial intelligence models to systematically investigate the role of RNA molecules in gene regulation and human disease. The central goal is to build a predictive framework capable of reconstructing and interpreting RNA‑mediated interactions at genome scale by integrating large experimental, interactomic and multi‑omics datasets.

For RNA‑RNA interactions, datasets generated by PARIS, RIC‑seq, MARIO, KARR‑seq and FANTOM‑related approaches will be used to reconstruct RNA regulatory networks and uncover previously unknown, potentially functional interactions. In parallel, CLIP‑seq and related datasets will be incorporated to model protein‑RNA interactions and to place RNA‑RNA contacts within the broader context of ribonucleoprotein regulation. These data‑driven approaches will be complemented by novel computational developments designed to improve interaction prediction, prioritization and biological interpretation.

The project will extend beyond classical RNA interactions to include RNA‑chromatin and RNA‑DNA associations. In this context, it will seek to identify RNA molecules that associate with specific genomic loci and to understand how these interactions contribute to chromatin organization and transcriptional regulation in three‑dimensional space. Particular attention will be given to long non‑coding RNAs, which are increasingly recognized as key regulators of genome architecture and gene expression.

Where suitable datasets become available, the framework will also explore more complex mechanisms, including triplex‑mediated RNA‑DNA interactions, potentially stabilized or mediated by protein factors.

ESSENTIAL REQUIREMENTS

PhD in computational biology, machine learning, bioinformatics, physics or related fields;

High proficiency level in programming languages (e.g., Python, R, SQL), in Linux environment, and in handling workflow managers and containers;

High proficiency level in Python and relevant deep learning libraries (such as Tensor Flow, PyTorch), along with experience in structural modeling tools (e.g., Vienna, Rosetta, RNAstructure) and graph neural networks or transformers applied to molecular…