• Raniero Pittini
  • Head of Research Medtech and Head of Engineering
  • Switzerland Innovation Park Biel/Bienne

Curriculum Vitae


PhD in Physics, ETH Zürich, Switzerland (1995)
Master in Physics, ETH Zürich, Switzerland (1990)

Professional Experience

Switzerland Innovation Park Biel/Bienne, Head of Research Medtech and Head of Engineering (since 2017)
Maxon Motor AG (Switzerland), Head of Research (2007-2017) and R&D Manager (2004-2007)
Diamond SA (Switzerland), R&D Manager (2000-2004)
Tohoku University and JST (Japan), Assistant Professor (1995-2000)
ETH Zürich (Switzerland), Research Assistant (1990-1995)

Research Interests

Machine learning and artificial intelligence
Sensors and actuators in medical devices
3D technologies in medical applications

Honors & Awards

Outstanding Research Award, Swiss Physical Society (1997)


Phys. Rev. Lett. 76 (1996) 3428
Phys. Rev. Lett. 77 (1996) 944
Phys. Rev. Lett. 78 (1997) 725


Product innovation and artificial intelligence

Switzerland Innovation Park Biel/Bienne is a research organization working, among others, on Medtech and Healthtech projects and with experience in fast product development. We report on the application of artificial intelligence in the development of several innovative products. In one of our projects, together with the company Resilient and with the iHomeLab, we are developing a device to measure stress and prevent burnouts. The transient signal of several sensors (applied on people) is transmitted to a smartphone, where a “stress score” is calculated in nearly real-time. We apply supervised deep neural network (DNN) learning with sympathetic- and parasympathetic- enriched data samples. Specialized burnout clinics provide the necessary medical expertise. In another project, “living well with Anne”, a personal assistant for elderly people with dementia is being developed with the company Virtask and with the iHomeLab with international project consortium. This personal assistant recognizes the cognitive capabilities and impairments of the elderly patients using machine learning algorithms and reacts adaptively selecting the assistance programs appropriate for each patient.