Student assistant position (f/m/d) in Computational Neurology Computational Neurology

Job Description

We invite applications for a student assistant for contributing to developing models of personalized neurostimulation and biomarkers of neurodegenerative diseases.

The student assistant will perform the following tasks:
•    Preprocessing of neuroimaging data (fMRI, EEG) and implementation of processing pipelines
•    Coordination, planning and delivery of administration for the group
•    Preparation/Groundwork for a grant proposal
•    Maintaining and updating the section website

Prerequisites: Python programming, completed courses on neuroimaging and/or computational neuroscience

Employment conditions that we offer:
- An inspiring international research environment characterized by a strong focus on academic rigor and publication of results
- Intensive training in both computational methods and clinical research
- Possibility of a master's thesis
- Participation in the group's seminar and INI activities for further training in computational neuroscience

Interested candidates are asked to send their applications including CV and a brief motivational statement of research interests to Prof. Dr. Xenia Kobeleva. Preference will be given to earlier applications. 

Ruhr University Bochum is committed to equal opportunity in employment and gender equality in its working environment. To increase gender distribution in all job categories and at all levels, we strongly encourage applications from qualified women. Female applicants will be given preferential consideration when their level of qualification, competence and professional achievements equals that of male candidates, unless arguments based on the personal background of a male co-applicant prevail. Applications from appropriately qualified handicapped persons are also encouraged.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210