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Postdoctoral Researcher on fMRI study of spontaneous thoughts and natural task

We seek a talented neuroscientist with extensive experience in the areas of advanced fMRI analysis, (such as encoding or decoding models) and the design of experiments using naturalistic tasks and assessing spontaneous thoughts. A 2.5-year postdoctoral position is available at Dr. Mingbo Cai’s lab at the International Research Center for Neurointelligence, The University of Tokyo. You will work on an exciting international collaboration project co-funded by AMED of Japan and DFG of Germany directed by both Dr. Cai and Dr. Nicolas Schuck at Max Planck Institute for Human Development. You will also have the opportunity to collaborate remotely with Dr. Quentin Huys at the University College London. The project will focus on understanding the contents and dynamics of spontaneous thoughts using fMRI decoding and natural tasks. In the later stage of the project, we also plan to examine the clinical implication of differences in spontaneous thoughts. Applicants with experience working on populations with mental disorders are also highly encouraged to apply. The working language is English. The working location is at The University of Tokyo, Japan.

Contract duration: renewed annually up until Jan 2024 (future renewal depends on performance and budget), with a probation period of 6 months.

Starting date: negotiable

Requirements

(1)   Applicants must hold, or be expected to hold by the time of appointment, a PhD, M.D. or equivalent degree in Neuroscience, Psychology, Psychiatry, Machine Learning, Statistics or related fields.

(2)   Experience in conducting fMRI experiments and data analysis (evidenced by publications, conference presentations or writing samples).

(3)   Familiar with at least one of the following tools: AFNI, FSL, nilearn, BrainIAK.

(4)   Good speaking and scientific writing skills in English.

(5)   Good programming skills.

(6)   Willing to collaborate and help colleagues.

Requirements 2 and 3 may be replaced by two or three of the preferred skills below:

Preferred skills:

(1)   Research experience with fMRI experiments involving natural tasks and multivariate pattern analysis.

(2)   Experience with experiments investigating spontaneous thoughts.

(3)   Research experience with investigations of psychiatric disorders.

(4)   Knowledge of and experience with deep learning.

Application procedure:

Please send an email with title “[Postdoc Application] + your name” to mingbo[doc]cai[at]ircn[dot]jp. Please include a cover letter describing your background and research interest, a curriculum vitae, and contact information for 2-3 recommendation letters (ideally you can also request your referees to send their letters directly to the above email address). To increase the chance of being considered, we suggest you include writing samples or a copy of a poster. Informal inquiry can also be sent to Dr. Cai.

Application deadline:

The application deadline is July 31. The position will remain open until filled. Review of applications starts as soon as received.

Other opportunities

Students: If you are a Master or PhD student of the FoPM program of the University of Tokyo, Dr. Cai can be a co-supervisor if you are interested in conducting research in our lab, especially focusing on (but not limited to) computational psychiatry. There is an exciting opportunity to work in between deep learning and computational psychiatry. We also welcome students at various stages in The University of Tokyo or surrounding area to join research on a part-time basis. There is also an opportunity for graduate students from Tsinghua University to visit and conduct research at IRCN. Please contact Dr. Cai for details.

We cannot sponsor more full-time research assistants at the moment. However, if you have external funding to support your visit to the lab or want to seek collaboration or co-mentorship or a summer internship, please feel free to contact Dr. Cai and explain your research interest. Please note that we are more interested in human intelligence than simulating neuronal models or merely seeking applications of machine learning tools or data mining.

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