Postdoc position "Accurate detection of precise spiking motifs in neurobiological data"
Application deadline :
Applications are invited for a fully-funded postdoctoral position at INT in Marseille, France, in the field of machine learning to develop an algorithm for the accurate detection of precise spiking motifs in neurobiological data.
HomeNeurojobsPostdoc position “Accurate detection of precise spiking motifs in neurobiological data”
Applications are welcome for a fully funded post-doctoral position at INT in Marseille, France. Your mission will be to build an algorithm for the accurate detection of precise spiking motifs in neurobiological data. The project is funded by the polychronies grant (AMX-21-RID-025) and will be coordinated by Laurent Perrinet along with Thomas Schatz (theory) and Rosa Cossart (neurobiology).
To obtain further information, please visit https://laurentperrinet.github.io/post/2023-05-01_postdoc-position_polychronies. To candidate, contact me @ Laurent.Perrinet@univ-amu.fr and provide a synthetic letter of motivation and a CV. I always respond, usually in less than one week, so please contact me again in case you did not hear from me. Ideal starting date is October 1st, 2023, but may be flexibly arranged to suit the candidate. The appointment is for 18 months and applications are welcome immediately.
Candidates should have experience in computational neuroscience, physics, engineering, or related fields, and a strong background in machine learning. A multidisciplinary background would be highly appreciated, especially an advanced knowledge of mathematics.
The candidate must have good computer science skills (programming skills, git versioning, …) Python programming experience is required. The candidate must have a strong interest in neuroscience. The candidate must be fluent in English and willing to proactively interact with partners in different communities, including theoretical neuroscience, machine learning, or neurobiology. The preferred candidate should have the ability to work autonomously and be flexible to adapt to the working methods of the supervisors.