ESSENTIAL REQUIREMENTS

• A PhD in Computer Science, Machine Learning, Computer Vision, Physics, Engineering, Mathematics or related areas;
• Documented expertise in:
• Computer Vision, with focus on multimodal learning;
• Deep generative models, e.g., GANs, diffusion models, encoder-decoder architectures, or optimal transport models;
• ML and CV approaches with a preference for former and recent deep learning models (GNNs, Transformers, etc.).
• Strong programming ability (Python preferred) with hands-on skills in AI and Deep Learning frameworks (e.g., Pytorch, Tensorflow or equivalent tools);
• Spouse the mainstream AIGO research line above quoted;
• Ability/willingness to integrate within multidisciplinary research group;
• Proven strong track publications record in the relevant technical areas;
• Ability to properly report, organize and publish research data;
• High motivation to learn;
• Good priority management;
• Fluency in spoken and written English.

ADDITIONAL SKILLS

• Knowledge/experience on multimodal approaches, and possibly on (some of) the topics above quoted (e.g., domain adaptation, few-shot learning, self-supervised learning, model debiasing, etc.);
• Experience with deep learning approaches applied to different science domains, such as chemistry, materials, physics, imaging, drug discovery, climate/weather forecast, etc.;
• Knowledge of 3D vision (multi-view geometry, point cloud data processing, neural rendering, Gaussian splatting, etc.);
• Knowledge of graph neural networks;
• Experience in deploying and fine-tuning DL models, also large language or vision-language models;
• Practical experience on deploying ML models on HPC platforms;
• Team player skills with the ability to communicate technical knowledge in a clear and understandable manner;
• Capacity to work autonomously and collaboratively in a challenging highly interdisciplinary environment;
• Possess analytical reasoning skills and a growth mindset.

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 Artificial Intelligence for Good (AIGO) Research Unit is coordinated by Prof. Vittorio Murino. It 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 such as biomedical, healthcare, and several others ultimately leading to people wellbeing.
Specifically, AIGO aims at studying learning paradigms in presence of imperfect data, especially in multimodal scenarios, hence tackling unsupervised, semi-supervised and self-supervised settings, weakly or noisy labeled data, few, class imbalanced, or biased. Domain adaptation and generalization, few/zero-shot learning, learning with biased data, and continual learning, even extended to multimodal scenarios, are among the major areas to be investigated, also given their valence in tackling practical, real-world applications.
AIGO will also consider generative AI models, especially related to the most recent trend regarding multimodal foundation models, including large language models (LLMs) and vision and language models (VLMs). From a general standpoint, AIGO will also consider architectural and computational issues addressing the so-called lightweight Machine Learning in order to design low-power ML technologies and approaches dealing with machine intelligence at the very edge of the cloud (e.g., for robots). Given the focus on people wellbeing, we clearly aim at the design of AI methods considering at their ground ethical, privacy and fairness aspects, as well as their robustness, towards the design of explainable, trustworthy and transparent deep learning techniques.
Main applications will involve biomedical, biological, neuroscience, and healthcare in general. Brain investigation (and its diseases) is identified as the main (but not exclusive) area of interest. Ultimately, AIGO will seek to develop models that can also be readily applicable to IIT interdisciplinary research, ranging from neuroscience to robotics, in particular by leveraging our in-house robotics platforms (iCub, ErgoCub, R1 et al.), IIT neuroscience teams, and HPC computational facilities.
AIGO benefits from the collaboration with several universities and research centers worldwide, most often with the closer universities of Genova and Verona. AIGO is part of ELLIS (https://ellis.eu/) – an European network of excellence in AI, Machine Learning (ML) and Computer Vision (CV), of which Prof. Murino is a Fellow member.

Within the research team, your main responsibilities will be:

• Pursue of research in some of the above-mentioned topics addressed by AIGO Research Line, at both individual and collaborative level. Interdisciplinarity research along IIT Flagship programs is also an important aspect in AIGO and in IIT in general;
• Supervision of the research activities of PhD students;
• Publications on major conferences and top journals;
• Search and preparation of funding opportunities, e.g., project proposals to apply to national and international grants, as well as to acquire funds from industrial partners.