ESSENTIAL REQUIREMENTS

• A MSc in computer science, engineering, robotics, or related fields
• Proficiency in C++, Python
• Good knowledge in Deep Learning
• Good command of spoken and written English

ADDITIONAL SKILLS

• Background in neuromorphic technology, event-driven sensing
• Good communication skills
• Strong problem-solving attitude
• High motivation to learn
• Spirit of innovation and creativity
• Adeptness at time and priority management
• Ability to work in a challenging and international environment
• Ability to work independently and collaboratively in a highly interdisciplinary environment

We have an open position for a talented fellow interested in developing novel technologies for rehabilitation of motor and cognitive impairments. We will combine and compare different AI-based technologies with neuromorphic computing for assessment of motor and cognitive functions. The analysis of movements in goal directed actions and its variations from typical patterns will support monitoring and tailoring of rehabilitation therapies. All technologies and their combinations will be benchmarked in their efficacy in assessing the condition/disease for their application to clinical scenarios, analyzing trade-offs between accuracy, power, online processing, continuous monitoring, etc.
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 a shared research goal.
The Rehab Technologies Research Unit is coordinated by Matteo Laffranchi and focuses on research in technologies for health. The position is in cooperation with the Event Driven Perception for Robotics (EDPR) Research Unit, with extensive experience in neuromorphic computing.

Within the research team, your main responsibilities will be:

• Refine and setup a multi-technology system for human motion tracking;
• Dataset acquisition with trial subjects performing movements;
• Develop, train, and test multisensory classification networks for goal directed actions recognition;
• Compare the assessment performance of the above networks when using different sensors;
• Develop a method to assess the movement impairment and improvement induced by rehabilitation.