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PhD Student (f/m) in Cognitive and Clinical Neuroscience

Published 29 March 2018 (330 days ago)
Deadline for applications 30 April 2018 (298 days ago)
Part-time position
Technische Universität Dresden / Universitätsklinikum Carl Gustav Carus Dresden
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Within the Technische Universität Dresden, the Section of Systems Neuroscience is closely associated with the Departments of Psychiatry and Psychology and the Neuroimaging Center, which offers excellent research collaborations and infrastructure such as a 3 Tesla MRI scanner for full-time research, MRI-compatible EEG and eye tracking as well as TMS and TDCS. Our international and multidisciplinary group conducts cognitive neuroscience research at the cutting edge to clinical applications. In order to better understand human action control we investigate cognitive processes such as executive functions, learning, decision making, or motivation, and the function of neural systems involved herein. On the clinical side we study how these neuro-cognitive mechanisms contribute to the complex behavioral dysfunctions observed, for example, in addictive behavior. In its 2nd funding period the DFG funded Research Unit 1617 ’Learning and Habitisation as Predictors of the Development and Maintenance of Alcoholism’ is a joint effort of the Charité Berlin and the Technische Universität Dresden and comprises of 8 projects with a budget of over 2 Mill. €. The center combines expertise from psychiatry, clinical and experimental psychology, as well as cognitive and computational neuroscience to investigate role learning mechanisms in the etiology of alcohol use disorders.
Within this CRC, we invite applications for Project 1 on ‘Learning and decision-making as predictors for the development of alcohol use disorders’ as

PhD Student in Cognitive and Clinical Neuroscience (f/m)

The salary is according to the E13 TV-L dispositions (65 %). Contract is initially limited till July 2020.
The successful candidate will be involved in follow-up assessments of participants as well as project and data management duties. He or she will conduct comprehensive analyses of behavioral and MRI data, including computational modeling and model-based fMRI. Furthermore, the successful candidate is expected to play a key role in the publication of the results.
Requirements:
  • Excellent graduate degree (Diploma; MSc or equivalent) in any scientific field of study with a strong curriculum in neuroscience (i.e. psychology, medicine, neuroinformatics)
  • Experience in scientific work (e.g. as student research assistant)
  • Sufficient language skills to interact with local participants in German and the global scientific community in English (excellent language skills are a plus)
  • Keen interest in experimental approaches to study complex human behavior
The ideal candidate would additionally be characterized by:
  • Previous hands-on experience in neuroscience in general and in fMRI in particular
  • Expertise in computational modeling of behavioral data (e.g. reinforcement learning)
  • Programming skills in any common software environment (e.g. Python, MATLAB, E-Prime / Presentation / PsychToolbox)
  • Sharing our passion to push the limits in understanding the brain
We offer you the possibility of:
  • Being part of the structured PhD graduate program the CRC 940
  • Working in an interdisciplinary team contributing to our understanding of the brain with the particular skills that you already have and will further develop
  • Arranging for flexible working hours to find a balance between work and family life
Women are explicitly invited to apply. Disabled persons will be preferred in case of equal qualification.
Complete applications (including a full CV and names and contact information for two or three referees) can either be loaded up here or sent as one PDF-document via email with Registration-Number PSY0918080 in the email subject line to Prof. Dr. Michael Smolka (systems.neuroscience@tu-dresden.de). Application deadline is April 30. For more information about the position please contact us via email or telephone (+49 351 463 42201).
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Published on academics.de on 29 March 2018

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