Manuel Reis Carneiro‘s thesis “High‑fidelity on‑skin bioelectronics through advanced soft and sustainable materials” has been awarded the 2025 Best Ph.D. Thesis Award by the Applied Sciences MDPI journal.
Launched in 2021, the Best Ph.D. Thesis Award recognizes young researchers for outstanding doctoral work in applied sciences, encouraging them to continue advancing their field through exceptional scientific contributions.
Manuel enrolled in the CMU Portugal Program in 2020 and graduated in 2025 earning his dual degree Ph.D. in Electrical and Computer Engineering from Carnegie Mellon and Universidade de Coimbra.
Manuel stated that “working across the Carnegie Mellon University’s College of Engineering, the Electrical and Computer Engineering Department of Universidade de Coimbra (DEEC), the Institute of Systems and Robotics – Coimbra and the Faculdade de Ciências e Tecnologia da Universidade de Coimbra (FCTUC) shaped both the scientific direction of this work, and the way I think about soft bioelectronics, materials, and physiological sensing.”
His research was supervised by Professor Mahmoud Tavakoli at the University of Coimbra and Professor Carmel Majidi at CMU, to whom he expressed deep gratitude for their “guidance, trust, and scientific vision that made this work possible”.
Manuel is now continuing his research on soft bioelectronics and lecturing at ETH Zürich, in Switzerland.
Read the MPDI’s official announcement here and Manuel’s awarded Ph.D. thesis is available here.
Other articles featuring Manuel Carneiro:
- In the media: Manuel Reis Carneiro featured in “90 segundos de ciência”
- CMU Portugal researchers at UC awarded for their Innovative Research
- Acelera@UC distinguished Manuel Reis Carneiro for innovative idea developed under CMU Portugal Project
- CMU Portugal Inside Story: Manuel Reis Carneiro
- Eco-friendly conductive ink promises to revolutionize the production of soft stretchable electronic circuits
- Stretchtonics’ researcher, Manuel Carneiro, has created a flexible, reusable and low cost electroencephalogram