Postdoctoral Position in Multimodal and Generative Artificial Intelligence Biomedical Imaging

Position: Postdoctoral Position in Multimodal and Generative Artificial Intelligence for Biomedical Imaging an
Commitment & contract
12 months

Location

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

At the Italian Institute of Technology (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 You will be working in a multicultural and multi-disciplinary group, where junior and senior scientists collaborate, each with their expertise, to carry out a scientific activity with shared research goals. The research focuses on fundamental AI topics from methodological and theoretical perspectives, yet functional to tackle a number of applications and actual case studies related to several domains.

The Artificial Intelligence for Good (AIGO) research unit is coordinated and led by prof. Vittorio Murino.

Specifically, AIGO research is centered on the development of advanced machine learning and deep learning methodologies for learning from complex, multimodal, and imperfect data, with particular emphasis on biomedical and healthcare-related applications. AIGO investigates learning paradigms under limited, noisy, or biased supervision, including unsupervised, semi-supervised, and self-supervised settings, as well as challenges related to domain shift, out-of-distribution data, data imbalance, zero/few-shot learning, open-set recognition, and continual learning.

The research also encompasses generative models and modern multimodal foundation models, alongside lightweight and efficient AI techniques for deployment in resource-constrained environments.

AIGO also benefits from collaborations with numerous international universities and research centers, particularly with nearby universities in Genoa and Verona. The group is also part of ELLIS ((Use the "Apply for this Job" box below).), a European network of excellence in Artificial Intelligence, Machine Learning, and Computer Vision, of which Prof. Murino is a Fellow.

For this position, the research activity involves the study of multimodal and generative models, representation learning, and data-driven approaches applied to medical imaging and heterogeneous biomedical data. The goal is to learn image-derived representations that capture phenotypic information and can be used as predictive proxies for underlying genotypic factors and genetic risk.

The emphasis is on developing models that leverage high-dimensional imaging data and contextual information to predict genotype-related information, going beyond traditional statistical association-based approaches.

Within the research team, your main responsibilities will be:

Develop and study machine learning models that link medical imaging data to latent biological or genotypic factors, under limited supervision and data heterogeneity;

Conduct research activities on key AIGO research topics, both independently and collaboratively;

Supervise the research activities of PhD students within the team;

Contribute to publications in high-level scientific journals and conferences;

Support the preparation of national, international, and industrial project proposals.

ESSENTIAL REQUIREMENT SA PhD in Artificial Intelligence, Machine Learning, Computer Vision, Computer Science, Engineering, Physics, Mathematics, or related disciplines;

Documented experience in Machine/Deep Learning and Computer Vision, preferably applied to medical or biomedical imaging, with particular attention to multimodal learning;

In-depth knowledge of generative models (e.g., GANs, diffusion models, encoder-decoder architectures, optimal transport models), including their use for representation learning or data modeling in scientific domains;

Strong command of modern deep learning approaches, including Graph Neural Networks (GNNs) and Transformers;

Excellent programming skills, preferably in Python, with hands‑on experience using AI and deep learning frameworks (e.g., PyTorch (preferred), Tensor Flow, or equivalent);

Strong publication record in relevant scientific venues;

Excellent written and spoken English.

ADDITIONAL SKILLS Knowledge or experience with multimodal approaches and topics such as domain adaptation, few/zero-shot learning, self-supervised learning, model debiasing, and continual learning;

Experience working with…