Maastricht University

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Maastricht University (UM) is one of the youngest and most international universities in the Netherlands. The university stands out for its innovative education model, international character and multidisciplinary approach to research and education. More than 50% of its 18,000 students and 40% of the scientific staff come from abroad, (representing more than 100 different nationalities). 50% of the scientific personnel is female. Research at UM is carried out in multidisciplinary teams and in close cooperation with institutes, businesses, industries, and organizations all over the world. UM is committed to have an impact by deploying high-level research and innovation to resolve societal challenges.

European and international themes are deeply rooted in its research and education as shown, for example, by the interfaculty ‘Maastricht, Working on Europe’ research programme. This creates an international atmosphere that is attractive to Dutch as well as international students and (research) employees. UM provides a vibrant and inspiring environment for researchers to carry out their project. Thanks to its high-quality research and study programs as well as a strong focus on social engagement, UM has quickly built up a solid reputation. UM attaches great importance to employability and mobility improvement of young researchers, offering opportunities to further develop themselves professionally and capitalize on their competences where possible. The Staff Career Centre supports them to improve their general competences, giving them the chance to maintain their employability and to further develop the qualities needed for their present position at the UM, or future roles. Researchers at the UM work in multidisciplinary teams, in close cooperation with international institutes, businesses and industries. Its high-quality researchers have attracted international attention by taking the lead in several large European research projects, and the UM research portfolio continues to attract national and international top researchers.

UM is a founding member of the Young European Research Universities Network (YERUN) and is coordinator of the European University Alliance ‘Young Universities for the Future of Europe’ (YUFE). UM was ranked 127th worldwide by the Times Higher Education (THE) for 2020, with strong scores in international outlook and research visibility. It was ranked 6th in THE ranking of young universities in 2020 and 19th in the QS ‘top 50 under 50’ ranking in 2020. In Horizon 2020, UM has hosted 33 MSCA Individual Fellowships and is involved in 32 ITN’s, of which UM coordinates 9. The total amount of projects in Horizon 2020 (from 2014 to June 2020) that the UM is involved in, is about 190 with approximately 100 M€ in funding volume.

Description of the partner’s main task in the project

The research group of UM involved in the project is located within the relatively new Maastricht Centre for Systems Biology (MaCSBio) at the new Faculty of Science & Engineering. The main expertise of the senior scientist involved are in experimental and computational neurosciences. The Jolivet team has decades of expertise in developing computational models of neurons and glial cells. It takes over from UNIGE with the same key personnel. UM’s role in IN-FET is thus 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. UM 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 UM 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).

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

Prof. R. Jolivet has access to the advanced computing infrastructure of UM for the computational part of WP2. Additionally, Prof. R. Jolivet has access through multiple collaborators within his new department (MaCSBio) to the expertise and facilities at the Faculty of Psychology & Neuroscience at UM, in particular at the Maastricht Brain Imaging Center (M-BIC) equipped with a 7T and 14T MRI scanners and the matching unique expertise.

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 Jolivet, Progress 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]

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