29 Jun 2020
29 Jun 2020
|Date:||29 Jun 2020|
|Location:||Champalimaud Centre for the Unknown|
|Job Type:||Full Time|
The Theoretical Neuroscience Lab is looking for a postdoctoral fellow to work on a project that involves the (1) analysis of neural population activities using machine-learning techniques, and the (2) modeling of the results with (spiking) neural networks. The ideal candidate should be able to spearhead one of these research lines, and to contribute intellectually to the other. Relevant background literature: Barrett et al (2016), eLife 5:12454; Kobak et al (2016) eLife 5:10989; Deneve and Machens (2016), Nat Neurosci 19:375-82; Semedo et al (2019), Neuron 102:249-59.
The proposed project requires a strong background in a quantitative field (physics, mathematics, computer science, and equivalent fields).
Questions about the position should be addressed to Christian Machens at: email@example.com
We are looking for any national, foreign, or stateless candidate(s) holding a Ph.D. degree in a quantitative field (physics, mathematics, computer science, engineering, and related fields), with a demonstrated ability for independent and creative research. Good analytical and coding skills as well as prior experience in fields related to computational neuroscience or machine learning are a bonus. Good English skills are essential.
Candidates must submit a single document (paper or PDF file) that contains a motivation letter and the Curriculum Vitae, both written in English, collectively not exceeding 4 pages. Candidates should also submit the contact details of two referees.
Applications should be submitted to Christian Machens at: firstname.lastname@example.org