Two postdoc positions in theoretical neuroscience and nonlinear time-series analysis
*Call for postdoctoral researcher**s* Two postdoc positions are available in (A) theoretical neuroscience and (B) nonlinear time-series analysis/control (or active inference) under the supervision of Prof. Hideaki Shimazaki. *(A) Theoretical Neuroscience* We've been constructing statistical analysis methods for neural data to test theoretical hypotheses on the information coding, underlying circuitry mechanisms, and principles of adaptation in the brain (e.g., Bayesian brain hypothesis, predictive coding, free energy principles). We use methods in statistical physics and machine learning. See references below for our theoretical approaches to analyzing the dynamics of a neural population. We are looking for a postdoc interested in conducting theoretical studies while working closely with neuronal spiking and/or omics data. The contract is yearly-basis and renewable *until March 31, 2026*. Workplace: Kyoto University, Kyoto, Japan *(B) Nonlinear time-series analysis and active inference* Our group strives to understand theoretical principles of organisms' perception and behavior in adapting to the environment and extend the knowledge to create intelligent agents. A postdoc position is available for this research area. We are looking for a postdoc interested in, e.g., Bayesian time-series analysis and control on nonlinear dynamics, the free energy principle, and active inference. The contract is yearly-basis and renewable *until March 31, 2025*. Workplace: Kyoto University, Kyoto, Japan A successful applicant should have a Ph.D. in theoretical neuroscience, machine learning, physics, applied mathematics, or engineering, but not limited to these. The applicant should demonstrate research experiences with a strong publication record. Programming skills in Python or willingness to learn it, will be valued. To apply, please send a CV, a statement of research interest (maximum two pages), and contact information of two references to hideaki.shimazaki@gmail.com. Please indicate which job you are interested in (A or B, or both). Either English or Japanese is accepted. The starting date is flexible, while earlier arrival is appreciated. Screening starts *from September 16th*. The salary follows the university rule and depends on the expertise of an appointed researcher. Please feel free to send me any informal questions regarding this post. Sincerely, Hideaki Shimazaki, PhD Email: hideaki.shimazaki@gmail.com Webpage: http://www.neuralengine.org Ref: Aguilera, M., Moosavi, S. A., & Shimazaki, H. (2021). A unifying framework for mean-field theories of asymmetric kinetic Ising systems. Nature communications, 12, 1197 <https://www.nature.com/articles/s41467-021-20890-5>. Donner C, Obermeyer K, Shimazaki H. Approximate inference for time-varying interactions and macroscopic dynamics of neural populations. PLoS Computational Biology (2017) 13(1): e1005309 <http://dx.doi.org/10.1371/journal.pcbi.1005309> Shimazaki H, Amari S, Brown EN, and Gruen S, State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data. PLOS Computational Biology (2012) 8(3): e1002385 <http://dx.doi.org/10.1371/journal.pcbi.1002385>
participants (1)
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Hideaki Shimazaki