14-16 October 2026
Lisbon, Portugal




Call for abstracts will open on 27 January 2026.

Apply for oral or poster presentations.


The Champalimaud Research Symposium 2026
(CRSy26) will gather an interdisciplinary community of researchers to discuss the interplay between the neural and immune systems in relation to cancer initiation, progression and therapy. This symposium will emphasise the dynamic interactions among tumour cells, neurons and immune components, and how these relationships impact tumour growth, metastasis and the tumour microenvironment.

Key topics will include mechanistic insights into neuro-immune signaling pathways, the influence of stress and innervation on tumor immunity, and how immune responses can affect neural activity within tumours and beyond.


Symposium Chairs

Carlos Minutti
Immunoregulation Lab, Champalimaud Foundation, Lisbon, PT

Henrique Veiga-Fernandes Immunophysiology Lab, Champalimaud Foundation, Lisbon, PT


Confirmed Keynote Speakers

Douglas Hanahan
EPFL, Lausanne, CH

Florent Ginhoux
Gustave Roussy, Villejuif, FR

CRSy is the main scientific symposium of the Champalimaud Research. Since 2017, it has fostered global dialogue among researchers across various disciplines, focusing on groundbreaking advancements in neuroscience, physiology and cancer.


Previous Editions

2024
2022



︎    ︎    ︎    

Máté Lengyel, PhD


University of Cambridge & Central European University

Cambridge, UK & Budapest, Hungary

Máté Lengyel is Professor of Computational Neuroscience at the Department of Engineering, University of Cambridge, and at the Department of Cognitive Science, Central European University. Previously, he studied biology for his MSc and neurobiology for his PhD at Eötvös University, Budapest. He was then a postdoc at the Gatsby Computational Neuroscience Unit, UCL, followed by a visiting research fellowship at the Collegium Budapest Institute for Advanced Study. He is fascinated by the brain's remarkable capacity to learn continuously about the environment and to use this knowledge flexibly to make predictions and guide its future decisions. His group studies learning and memory from computational, algorithmic/representational and neurobiological viewpoints. Computationally and algorithmically, his work uses ideas from Bayesian approaches to statistical inference and reinforcement learning to characterise the goals and mechanisms of learning in terms of normative principles and behavioural results. His work also involves performing dynamical systems analyses of reduced biophysical and neural network models to understand the mapping of these algorithms into cellular and circuit mechanisms. His group collaborates very closely with experimental neuroscience groups, doing in vitro intracellular recordings, multi-unit recordings in behaving animals, and human psychophysical and fMRI experiments.

︎ Learn more about the speaker here