Technician position for Computer Vision and Machine Learning

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Testo originale dell'annuncio (in inglese)

Technician position for Computer Vision and Machine Learning - IT | Istituto Italiano di Tecnologia Commitment & contract: Employee Technician 3rd level, up to 20 months, fixed term contract Location: Center for Robotics and Intelligent Systems (CRIS), IIT, Via San Quirico 19d, 16163 Genova (GE), Italia 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. You will be working in a multi-disciplinary group, where people with different backgrounds collaborate, each with their own expertise, to support IIT research activity. The Human-Robot Interaction and Interfaces (HRI²) Research line is coordinated by Arash Ajoudani . We develop intelligent robots that enable natural and human-centered interaction at both cognitive and physical levels, while leveraging advanced interfaces and AI-based algorithms to allow robots to perceive, interpret, and better understand the environment in which they operate. Building upon the development of AI-based perception algorithms that enable robots to understand and interpret their environment, the TEC4DPI project focuses on computer vision and machine learning techniques applied to the assessment of personal protective equipment (PPE). Specifically, the project aims to analyze images acquired through standard RGB cameras to estimate the usage and degradation state of PPE, providing a more objective and quantitative evaluation of their condition. This position focuses on the development of the experimental and computational pipeline supporting the project objectives. Activities include the design and implementation of a controlled camera-based acquisition setup, dataset generation and management, and the development of computer vision and machine learning models for PPE condition assessment. Designing and implementing a controlled camera-based acquisition setup for collecting image data related to PPE condition. Supporting the acquisition, annotation, and management of image datasets. Developing computer vision and machine learning algorithms for the analysis of PPE usage and degradation. Contributing to the development and validation of data processing and model training pipelines. Collaborating with senior researchers in the experimental evaluation of the developed methods. Maintaining clear documentation of datasets, code, and experimental procedures. Assisting in the preparation of technical reports, scientific publications, and project deliverables. A Master’s degree (MSc) in Artificial Intelligence, Data Science, Biomedical Engineering, Robotics, Computer Engineering, or a related field. Basic experience in machine learning and data analysis, with hands-on implementation (e.g., Python, TensorFlow, PyTorch, or similar frameworks). Familiarity with data preprocessing, visualization, and statistical analysis techniques. Knowledge of image processing techniques and familiarity with computer vision libraries (e.g., OpenCV). Strong attention to detail and structured approach to data handling. Knowledge of data-driven approaches for visual inspection and condition assessment. Experience in camera calibration an