A postdoctoral position is available at the Computational Systems Neuroscience Lab (Department of Computational Sciences, Wigner Research Center for Physics, Budapest Hungary) to work on a project focussing on integrating prior information with current evidence in early vision. The project is an HFSP-funded collaboration between four labs: Peyman Golshani's (UCLA, mouse electrophysiology and imaging), Máté Lengyel’s University of Cambridge, theory), and Wolf Singer’s Lab (ESI, Frankfurt, monkey electrophysiology).
The focus of the research project is how acquired knowledge can be integrated with incoming information during visual perception. Joining team members will work on novel population analysis techniques and/or develop computational theories of hierarchical computations. The position offers a chance to work actively with state-of-the-art machine learning techniques, to access advanced computing architectures and to get involved in cool experiments right from design of experiments, to develop predictions, and analysis of data.
The ideal candidate has a strong mathematical background, preferably with a PhD in physics, computer science, or mathematics or other quantitative disciplines. Besides mathematical skills, the position requires competence in programming (e.g. matlab, R, python, or C++). Candidates with training or research experience in statistics, machine learning, computational modelling, dynamical systems, systems neuroscience are especially encouraged to apply. Training in neuroscience is not required but the applicant has to demonstrate his/her willingness to acquire the necessary background for the project.
For informal inquiries please contact me by mail (
orban.gergo@wigner.mta.hu) or by arranging a meeting at CCN19 in Berlin (our posters are PS-1B.35, PS-2A.19, PS-2A.40,
PS-2B.44).
Please send applications, including CV, a research statement and contact information of two references by email to Gergo Orban before 20 October.
or sample our recent papers:
Gáspár ME, Polack P-O, Golshani P, Lengyel M, Orbán G (2019)
Representational untangling by the firing rate nonlinearity in V1 simple cells
eLife, 8:e43625 doi: 10.7554/eLife.43625
Bányai M, Lazar A, Klein L, Klon-Lipok J, Stippinger M, Singer W, Orbán G (2019)
Stimulus complexity shapes response correlations in primary visual cortex
Proc Natl Acad Sci USA, 10.1073/pnas.1816766116.
Orbán G, Berkes P, Fiser J, Lengyel M (2016)
Neural variability and sampling-based
probabilistic representations in the visual cortex.
Neuron, 92:530–543
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Gergo Orban, PhD