Job ID: 6742
Post-doctoral position in Bochum/Germany
Position: Post-doctoral Position
Deadline: 30 April 2021
Institution: Ruhr University Bochum
Prof. Sen Cheng, Institute for Neural Computation at the Ruhr University Bochum, invites applications for a full time (currently 39.83 hours/week) Postdoctoral position (TV-L E13) in Computational Neuroscience. The position starts on July 1, 2021 and is funded for three years.
The position is part of the Collaborative Research Center “Extinction Learning” (SFB 1280) and expected to study the principles underlying spatial learning and its extinction with reinforcement learning models. A particular focus is the role of episodic-like memory in learning and extinction processes.
The position is third party funded and does not have any formal teaching duties attached.
The research group is highly dynamic and uses diverse computational modeling approaches including biological neural networks, cognitive modeling, and machine learning to investigate learning and memory in humans and animals. For further information see www.rub.de/cns.
Please send your application, including CV, transcripts and research statement electronically, as a single PDF file, to firstname.lastname@example.org. In addition, at least two academic references must be sent independently to the above email address. The deadline for applications is April 30, 2021.
The Institute for Neural Computation is a central research institute at the Ruhr-University Bochum, see https://www.ini.rub.de/. It focuses on the dynamics and learning of perception and behavior on a functional level but is otherwise very diverse, ranging from neurophysiology and psychophysics over computational neuroscience to machine learning and technical applications.
The Ruhr-Universität Bochum is one of the leading research universities. The university draws its strengths from both the diversity and the proximity of scientific and engineering disciplines on a single, coherent campus. This highly dynamic setting enables students and researchers to work across traditional boundaries of academic subjects and faculties
previous job ID: 28684