The University of Geneva

Description of the Legal Entity

Logo UniGe

Founded in 1559 by Jean Calvin, the University of Geneva (UNIGE) is dedicated to thinking, teaching, dialogue and research. It is Switzerland’s second largest university with more than 17’000 students of 150 different nationalities and no less than 3950 researchers (including 595 professors) of 113 nationalities, who study and work in 9 different faculties (Science; Medicine; Humanities; Law; Theology; Psychology and Educational Sciences; Economics and Management; Social Sciences; Translation and interpreting). 

The university enjoys a strong international reputation, both for the quality of its research (it ranks among the top institutions among the League of European Research Universities) and the excellence of its education. This success has been won in part due to its strong ties to many national and international Geneva-based organizations, such as the WHO, the International Telecommunications Union, the International Committee of the Red Cross, and CERN.

UNIGE offers more than 280 types of degrees and more than 250 Continuing Education programs covering an extremely wide variety of fields: exact sciences, medicine and humanities. As a result of its history and its strategic choices, the UNIGE made possible a diversity of research areas to emerge in which the institution excels. Therefore, its research strengths are life sciences (genetics, molecular and chemical biology, bio-informatics), chemistry, physics of elementary particles, astrophysics and also some specific fields in social sciences and humanities. Due to its high-level standards in research and its many disciplines, the UNIGE is ranked among the best three, generalist, French-speaking universities, and among the 150 best universities in about 20’000 universities worldwide.

The scientific performance of UNIGE researchers is also expressed by the number and amount of subsidies obtained on a very competitive basis from the Swiss National Science Foundation and the European Commission. The UNIGE is one of the best Swiss institutions of higher education for the allocation of these funds.

At a national level, the UNIGE is one of the Swiss leaders for hosting National Centres of Competence in Research (NCCR). One of the main instruments of the Swiss National Science Foundation, these NCCRs foster scientific networks at an international level of excellence from fundamental to applied research. Each NCCR is under the directorship of a research institution (Leading house) which allows research groups based at the home institution to network with other teams working throughout Switzerland. Since 2001, 8 NCCRs have been hosted by the UNIGE as leading or co-leading house: "Frontiers in Genetics", "MaNEP", "Affective Sciences", "Chemical Biology", "LIVES – Overcoming vulnerability", "The synaptic bases of mental disease", "SwissMAP - The Mathematics of Physics" and "PlanetS".

Open to the world, the UNIGE leads research projects in collaboration with almost 100 countries. At a European level, the UNIGE actively participates in many EU research programs, particularly to Framework Programmes for Research with 250 participations in FP7 including 19 coordinations and 35 prestigious European Research Council Grants and over 50 participations in Horizon 2020 projects. UNIGE is also involved in 50 COST networks and research projects and many other European and international research and innovation programs (IMI, ESA, INTERREG, NIH, etc…).

Description of the partner’s main task in the project

UNIGE’s main role in IN-FET is in the simulation of neurons interfaced with the hardware device, and in the detailed analysis of signals recorded from, and sent to, the biological part of the novel technological platform delivered in this project.  UNIGE will contribute its expertise in computational neuroscience, neurophysics and neuroenergetics, employing detailed experimentally-calibrated multicompartment Hodgkin-Huxley-type models of cultured neurons as well as network models of simplified neurons and deep neural networks for machine learning, with a minor contribution to the interpretation and analysis of experimental data collected by SISSA.

Description and CV of the personnel carrying out the project

Prof. Renaud B. Jolivet (male) Joint Titular Professor (2016) will coordinate the contribution of UNIGE to IN-FET. Physicist (2001), received his PhD in Computational Neuroscience at EPFL (Switzerland) in 2005. Since 2016, he holds a joint appointment in biomedical physics between CERN and the University of Geneva. Prior to that, he was a fellow at the Universities of Lausanne and Zürich, a guest researcher at the University of Kyoto and RIKEN Brain Science Institute, then a fellow at University College London. Throughout his postdoctoral studies, he was supported by prestigious fellowships including a Marie Skłodowska-Curie fellowship from the European Commission. His work has been published in leading neuroscience journals such as Neuron (IF2017 14.32), Progress in Neurobiology (IF2017 14.16) and Current Biology (IF2017 9.25). His leadership has been recognized by him being elected a Board Member of the Marie Curie Alumni Association (2018), a project of the European Commission, and a member of the Board of Directors of the Organization for Computational Neuroscience (2019). Since 2016, his team has acquired more than 1M€ in competitive extramural funds at the national and European levels. 26/41 works published (Scopus/Google Scholar); h-index: 14/18 (Scopus/Google Scholar); cites 1324/2024 (Scopus/Google Scholar). ORCID: 0000-0002-5167-0851; ResearcherID: A-9883-2008; Scopus: 9743375700; Google Scholar: 9Ozwv7EAAAAJ. He has, or is advising 6 PhD theses (4F, 2M). 

Prof. Tobias Golling (male) is an experimental particle physicist with interests in machine learning applications.  He has been working in high energy particle (HEP) physics for the last eighteen years, and has focused on machine learning solutions to HEP problems for the last six years.  As of 2005 he is working on the ATLAS experiment (which discovered the Higgs boson in 2012 jointly with the CMS experiment) using the data provided by CERN’s LHC collider.  As of 2014 he is an Associate Professor of Physics at the University of Geneva, after having been and Assistant and Associate Professor of physics at Yale University for five years.  From 2005-2009 he was a postdoctoral fellow at Lawrence Berkeley Lab supported by the Humboldt foundation as a Feodor Lynen Fellow, and his PhD he received from the University of Bonn in 2005.  Among others Prof. Golling has been awarded the prestigious Cottrell Scholar Fellowship and Alfred P. Sloan Research Fellowship.  He is co-author of over 800 peer-reviewed publications in international scientific journals.

Dr. Dmytro Grytskyy (male) finished his studies in theoretical physics in Hamburg, Germany in 2011 and started his PhD in computational neuroscience at the Research Center Juelich & RWTH University Aachen, Germany in the group of Markus Diesmann. Later, he worked as a postdoc in the group of Memmersheimer in FIAS, Frankfurt, Germany, and now in the group of Prof. R. Jolivet at the University of Geneva, Switzerland. His research interests are the theoretical consideration of learning processes and corresponding network dynamics, bridging local neuronal characteristics and global effects on the network level. In particular, he is interested in network mechanisms of processing of external input on different timescales. He is experienced in methods from computational neuroscience, theoretical physics (path integration, stochastic equations, non-linear and chaotic systems) and information theory (transfer entropy, graphical models, WTA networks) applied to plastic neuronal networks. He is investigating inference of causal and contextual relations in given inputs, and optimization of information inference per energy spent via learning rules. He also has interests in machine learning, and in its intersection with biological neural networks.

List of previous projects/activities relevant to IN-FET

  • Australian Research Council (DP180101494), “How do myelinating cells alter brain circuits to facilitate learning?”, R. Jolivet, Partner Investigator 
  • Swiss National Science Foundation (31003A_170079), “Synaptic trading: energy savings versus information”, R. Jolivet, Principal Investigator (
  • H2020 (642889), “MEDICIS-Promed”, R. Jolivet, Participant (
  • FP7 (274384), “BrainEnergyControl”, R. Jolivet, Principal Investigator (
  • National Health and Medical Research Council, “Neuron-to-glia signalling: learning how synaptic signalling can promote CNS remyelination”, R. Jolivet, Partner Investigator
  • Swiss National Science Foundation, "New physics searches with b-tagging, top-tagging and machine learning, and HLLHC pixel upgrade using ATLAS at LHC", T. Golling, Principal Investigator
  • IVADO, "Machine learning for the analysis of the Large Hadron Collider Data at CERN", T. Golling, Principal Investigator

Description of infrastructures relevant for IN-FET

The labs of Profs. Renaud Jolivet and Tobias Golling at UNIGE have dedicated office and lab space at the École de Physique, Faculty of Sciences, University of Geneva.  The labs are also supported by baseline funding from the Department of Nuclear and Corpuscular Physics and by core computing infrastructures for parallel computing and machine learning, specifically access to large CPU and GPU farms and multi-TB storage for intensive computing tasks. Additionally, R. Jolivet has access to numerous worldwide facilities for organising management and dissemination activities.

Relevant publications 

  • Pivotal role of carnosine in the modulation of brain cells activity: Multimodal mechanism of action and therapeutic potential in neurodegenerative disorders (2019) G Caruso, F Caraci, R B JolivetProgress in Neurobiology (in press)
  • Microglial Ramification, Surveillance and Interleukin-1β Release are Regulated by the Two-Pore Domain K+ Channel THIK-1 (2018) R Jolivet*, C Madry*, V Kyrargyri*, I L Arancibia-Carcamo*, S Kohsaka, R Bryan, D Attwell, Neuron 97(2) 299-312 
  • Energy-Efficient Information Transfer by Visual Pathway Synapses (2015) R Jolivet*, J J Harris*, E Engl, D Attwell, Current Biology 25(24) 3151–3160
  • Multi-Timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble (2015) R Jolivet, J S Coggan, I Allaman, P J Magistretti, PLOS Computational Biology 11(2) e1004036
  • Synaptic Energy Use and Supply (2012) R Jolivet *, J J Harris*, D Attwell, Neuron 75(5) 762-767
  • Development of a new and much faster ATLAS calorimeter simulation using generative models, in particular Variational Auto Encoders [PASC18 presentation]
  • Addressing CPU-limitation in track finding using machine learning, in particular unsupervised learning, clustering, metric learning [TrackML challenge: , presented at ICHEP18, Connecting the Dots workshop 2017 & 2018, EPJ Web Conf. 150 (2017) 00015]
  • Flavor-tagging using machine learning and deep learning algorithms [ATL-PHYS-PUB-2017-013]
  • Boosted-object (W/Z/H/top) tagging using machine learning, [ATL-PHYS-PUB-2017-004; ATLAS-CONF-2017-064]
  • Charm-tagging with machine learning [ATL-PHYS-PUB-2015-001; ATL-PHYS-PUB-2017-013]