Janos Vörös

Prof. Dr. Janos Vörös

Position

Deputy head of Institute for Biomedical Engineering

Relation

Full Professor

Contact Data

Tel.: +41 44 632 59 03
Fax: +41 44 632 11 93
Tel. (Sec.): +41 44 632 45 85

http://www.lbb.ethz.ch
ORCID: 0000-0001-6054-6230

Address

ETH Zürich
Janos Vörös
Inst. f. Biomedizinische Technik
GLC  F 12.1
Gloriastrasse 37/ 39
8092 Zürich
Switzerland

Organisations Full Professor at the Department of Information Technology and Electrical Engineering
http://www.ee.ethz.ch/
Curriculum Vitae (CV)

János Vörös is a Full Professor in the Institute for Biomedical Engineering of the University and ETH Zurich (Department for Information Technology and Electrical Engineering) heading the Laboratory for Biosensors and Bioelectronics since 1st of January, 2006.

János Vörös has studied Physics at the Eötvös Loránd University in Budapest. After receiving a diploma in Physics in 1995, he was a doctoral student at the Department of Biological Physics of the Eötvös University (in collaboration with Microvacuum Ltd.) where he received his PhD in Biophysics in 2000. Since 1998 he was a member of the BioInterface group in the Laboratory for Surface Science and Technology at the Department of Materials of ETH Zurich as visiting scientist, postdoc, and from 2004 as group leader of the Dynamic BioInterfaces group until 2006. 

Prof. Vörös is interested in research and teaching in the areas of Bioelectronics, Biosensors and Neuroscience. His group performs interdisciplinary research at the interface between engineering, nanotechnology, materials science, medicine, and biology. The generated knowledge is then applied for developing new nanoscale tools (e.g. the FluidFM) and methods for biosensing, diagnostics, and interfacing biology. The group also develops new biomedical devices using stretchable, hybrid bioelectronics.

Besides his application oriented activities, Prof. Vörös is also interested in answering basic research questions about how our brain processes and stores information using controlled neural networks that form the basis of a new scientific area: bottom-up neuroscience.

Course Catalogue
227-0085-38L P&S: Controlling Biological Neuronal Networks Using Machine Learning
Publications inResearch Collection – Publication platform of ETH Zurich
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