The Medical Physics Section is embedded in the Department of Biomedicine and Prevention and in the Faculty of Medicine of the University of Rome Tor Vergata. 

As medical physicists, we work interdisciplinarily with life scientists, clinicians, engineers, mathematicians and computer scientists to devise and apply methods able to extract quantitative and predictive information of value in a biological, biomedical and clinical context. Broadly, our research topics include neuroimaging and neurostimulation physics, physiological systems modelling, systems medicine, modeling complex systems, artificial intelligence and machine learning, biomedical signal and image processing, biostatistics and big data analysis. Our main field of applications are currently neurology and psychiatry, personality neuroscience, pathology, cardiology, anesthesiology, epidemiology and health impact assessment, nuclear medicine and radiation oncology.

Our research is currently funded by the European Commission (Horizon 2020), the Italian National Ministry of Health, the US department of defense (DOD) and the National Institutes of health (NIH). We are currently looking for talented prospective Ph.D. Students and/or PostDoctoral associates (salary commensurate to experience) to work within these projects in the fields described above. For additional information and contacts, please click here.

For a current list of our publications, please click here. Also, below is a wordcloud summarizing their abstracts.

Example expertise and topics include:

  • Neuroimaging physics (MRI, PET, CT), both experimental and analysis
  • Neuromodulation (Transcranial Magnetic Stimulation, Ultrasound, tDCS), both experimental and modeling
  • Diffusion MRI (experimental and modeling) for biomarker generation (e.g. noninvasive markers for neruoinflammation)
  • Deep learning and machine learning applied to biomedical data analysis (images, signals, sounds) for diagnostic and prognostic purposes
  • Ah hoc deep computational architectures tailored to physiological (sub)systems
  • Emotional personality and behavioural neuroscience (i.e. tying biomarkers, images signals to phenotypes and behaviourals traits)
  • Dynamical systems, Network /graph theory and brain connectivity
  • Multimodal data fusion through traditional and AI methods
  • Radiation therapy (withing radiation oncology) modeling and optimization – both theoretical and practical
  • Nanoparticle design and toxicity prediction towards a safe by design approach
  • Health impact assessment
  • Radiomics and computer vision for diagnostic and prognostic portposes
  • Biomarker generation and analysis for supporting clinical trials
  • Pharmacokinetic modeling for transitioning from in vivo to in silico research modeling
  • Systems Medicine and modeling of physiological systems
  • Biomedical image and signal processing for inferring mechanisms and for biomarker generation (e.g. for intraoperative risk prediction)