ONE FELLOW POSITION IN CONTINUOUS NONINVASIVE MONITORING OF SUBJECTS WITH MOTOR AND COGNITIVE DISABILITIES

• (25000010)
  Commitment & contract: Fellow, 18 months
  Location: Genova, Italy

ESSENTIAL REQUIREMENTS

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

ADDITIONAL SKILLS

• 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 advancing statistical methods for the analysis of multimodal data in the continuous assessment of individuals with motor and/or cognitive impairments. The candidate will work with large-scale multidimensional datasets, integrating kinematic and electrophysiological signals to identify potential biomarkers of different conditions. The role involves developing and applying state-of-the-art statistical and machine learning approaches to extract meaningful patterns, supporting clinical decision-making and personalized monitoring strategies. The candidate will also evaluate trade-offs in accuracy, computational efficiency, and real-time applicability to ensure the feasibility of continuous assessment in clinical and real-world settings.
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 Lab Research Unit is coordinated by Matteo Laffranchi and focuses on research in technologies for health.

Within the research team, your main responsibilities will be:

• Multidimensional dataset acquisition on volunteers;
• Development, training, and testing of multidimensional classification networks;
• Compare the assessment performance of the above networks using different technologies;
• Develop a method to continuously assess the state of the trial subject.