Post-Doctoral Position in Multimodal AI- Perception: Object Detection and Pose Estimation

Position: Post-Doctoral Position in Multimodal AI-based Perception: Object Detection and Pose Estimation [...]
Commitment & contract: collaboration contract

Location:

Genova (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 provide equal employment opportunities and foster 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 join the Artificial Intelligence for Good (AIGO) research unit, coordinated by Prof. Vittorio Murino, a multicultural and multidisciplinary research group in which junior and senior scientists collaborate toward shared scientific objectives, integrating strong theoretical foundations with application‑driven research.

AIGO develops advanced Computer Vision, Machine Learning and Deep Learning methodologies for learning from complex, multimodal, and imperfect data. The research emphasizes robust learning under limited, noisy, or biased supervision — including unsupervised, semi‑supervised, and self‑supervised paradigms — while addressing key challenges such as domain shift, data imbalance, and continual learning. The activity further encompasses generative models, modern multimodal foundation models, and lightweight, computationally efficient AI techniques designed for deployment in resource‑constrained environments.

Within this framework, AIGO will lead the development of the computer vision subsystem of the LILO (LIghtweight LOng‑Reach Robotic Arm System) project, a joint initiative between the Italian Space Agency (ASI) and the Italian Institute of Technology. Specifically, LILO aims to realize an advanced robotic platform for autonomous satellite in‑orbit servicing, supporting inspection, capture, manipulation, life‑extension of orbital assets, and active debris removal.

In the context of LILO, the contribution of AIGO will focus on the design and implementation of the Vision System, a core enabling component for autonomous on‑orbit operations. In this context, AIGO has to develop robust perception algorithms for object detection and 6‑DoF pose estimation of cooperative and non‑cooperative space targets, including space objects.

Your research will address the intrinsic challenges of the space environment — such as extreme illumination variability, specular reflections, partial occlusions, and stringent onboard computational constraints — through the development of advanced machine and deep learning models. A multimodal perception framework will be adopted, integrating heterogeneous sensing modalities (e.g., RGB cameras, LiDAR, event‑based and thermal sensors) to ensure robustness, accuracy, and repeatability.

RESPONSIBILITIES

Developing and studying computer vision, machine and deep learning‑based models for object recognition and pose estimation from multichannel sensory data.

Investigating object detection and perception approaches that operate under limited supervision and synthetic and/or heterogeneous data conditions (e.g., supervised, semi‑supervised, and self‑supervised settings).

Designing synthetic benchmarking environments and sim‑to‑real strategies for pose estimation of known and unknown objects.

Optimizing perception models for embedded deployment and hybrid onboard/ground architectures.

Conducting research on core AIGO topics, both independently and in collaboration with team members.

Supervising PhD students and contributing to the coordination of research activities.

Publishing results in leading international…