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We are always looking for talented, motivated researchers. If you are interested in working with us, check our open positions to apply to a specific opening, or send a spontaneous application by filling in the form.
All the research activities will be carried out at the University of Genova within a Machine Learning center across the Mathematics and Computer Science departments.
Post-doctoral fellows
Supervisors
Luca Calatroni - DIBRIS
luca.calatroni@unige.itAbout
Model-aware learning approaches for image reconstruction integrate the theoretical guarantees of model-based methods for inverse problems with the expressive capabilities of data-driven techniques. They are now widely used in imaging and frequently achieve state-of-the-art results when combined with appropriate optimisation schemes. Examples include optimisation-based methods (such as unrolling, plug-and-play) and physics-inspired generative approaches. When the underlying physics of the problem at hand is only partially known and/or highly complex and training data are limited, several difficulties may arise.
This project will focus on developing model-aware image reconstruction methods for partially unknown and potentially non-linear forward models in the presence of limited training data. To address data scarcity, we will employ self-supervised training procedures. Numerical validations will be performed on fluorescence 2D/3D+t microscopy data, in collaboration with the group of G. Vicidomini (IIT, Genova, Italy).
Expected starting date: May/June 25.
Duration & salary: The duration is 1 year, and renewable. The salary varies depending on experience and is commensurate to international standards.Perspective candidates should make an expression of interest before January 15, 2025.
Useful links
Supervisors
Francesca Odone - DIBRIS
francesca.odone@unige.itNicoletta Noceti - DIBRIS
nicoletta.noceti@unige.itAbout
Description:
This project aims at designing and developing video-based methods for constructing hierarchical representations of human actions and activities. The focus will be on deep learning architectures that utilize self-attention mechanisms to process sequences of human poses and motion primitives. The devised methodologies will address challenges in action recognition, activity analysis, and the prediction or anticipation of intentions. Potential application domains include robotics and rehabilitation, where model efficiency and interpretability are critical advantages.
Requirements:
PhD in Computer Science, Computer Engineering, Artificial Intelligence, Applied Mathematics, or related topics. Background in Computer Vision and Machine Learning is required.
Expected starting date:
The estimated start date is March 2024.Perspective candidates should make an expression of interest before December 20, 2024.
Useful links