Benefits of noise in the transmission and processing of
time-dependent stimuli by populations of sensory neurons
We are looking for a PhD candidate to work on signal processing in
recurrent neural networks, especially information filtering of
sensory signals and the functional role of intrinsic fluctuations
and cell heterogeneity. These aspects of neural information
processing will be explored by means of analytical calculations,
numerical simulations of network models, and the comparison to
experimental data (collaboration with Jan Benda, University
Tubingen, Germany). In order to get an idea about similar approaches
pursued in the past, have a look at our recent paper in PNAS [Grewe,
Kruscha, Lindner, and Benda PNAS 114 E1977 (2017)].
The successful candidate should have a degree in physics,
mathematics or computational neuroscience (a background in
neurobiology is desirable but not obligatory), programming skills
(C++, Python, LaTeX, Linux), an excellent command of the English
language, good communication skills and team spirit.
Funding is provided for three years, starting within the next six
months. For details on the doctoral examination process at the
Physics Department of Humboldt University Berlin, see https://fakultaeten.hu-berlin.de/en/mnf/wisskar/promotionen/index_html?set_language=en.
Applications, including a letter of motivation, a CV, and a list of
three potential referees should be sent by email to
me
benjamin.lindner@physik.hu-berlin.de
(cc to
nikola.schrenk@bccn-berlin.de)
The deadline for applications is April 18th 2018, however, later
applications might also be considered.
Kind regards,
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Benjamin Lindner
Professor for
Theory of Complex Systems and Neurophysics
Bernstein Center for Computational Neuroscience Berlin
Philippstr. 13, Haus 2, 10115 Berlin
Room: 1.17, phone: 0049(0)302093 6336
Department of Physics Humboldt University Berlin
Newtonstr. 15 12489 Berlin
Room: 3.408, phone: 0049(0)302093 7934
http://people.physik.hu-berlin.de/~lindner/index.html
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