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The Connectivity Group (lead: Prof. Dr. Svenja Caspers) of the Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich (Germany) is seeking applications for a
*PostDoctoral Research Position in Population Neuroimaging*
**
(E 13 TVöD; 39 hours/week)
The Connectivity Group focuses on the investigation of brain variability throughout the life span in relation to environmental and genetic factors, with a particular focus on the aging brain and taking advantage of large population-based cohorts who underwent MR neuroimaging (e.g. UK Biobank, German National Cohort, Human Connectome Project, 1000BRAINS). Specific aspects include structural, resting-state functional connectivity as well as advanced diffusion imaging for structural connectivity analyses, including graph-theory analyses. Large cohorts of thousands of subjects are processed using adjusted pipelines and workflows for high-throughput imaging analytics based on common software packages (e.g. FreeSurfer, FSL, MRtrix) on available high-performance computing systems of the Research Centre Jülich. Current interests cover topics in advanced statistical analyses and data-driven methods using machine-learning and deep learning approaches. These efforts are part of current projects within the Human Brain Project (HBP).
The successful candidate has a PhD in neuroscience, computer science, psychology, medicine or related fields and has experience with processing and analyses of MR neuroimaging data in larger cohorts. A strong interest and expertise in applying machine-learning to large neuroimaging datasets is highly desired to enable the candidate to conduct novel and innovative research projects to understand the normal variability of brain aging.
Your profile:
- deep knowledge of several software packages for processing of neuroimage data (e.g. FreeSurfer, FSL, MRtrix), also including respective python packages (e.g. NiPy, DiPy) and longitudinal pipelines
- profound expertise in multivariate statistical analyses, including usage of software for large-scale statistical analyses (e.g. R) and modelling of non-linear and longitudinal effects
- profound programming skills (e.g. Python) enabling high-throughput big data analyses
- knowledge of and interest in machine-learning and deep-learning techniques to be applied to large neuroimage datasets of population-based cohorts
- familiarity in working with large datasets
- strong publication track record
- ability to work independently
- high proficiency in English
The applicant will become part of an interdisciplinary team of PostDocs, PhD and MD students with various backgrounds. He / she will have ample possibilities to expand his / her own knowledge and expertise and interact and collaborate with colleagues from different fields, including big data analytics, genetics, epidemiology and microscopic analyses. With our group closely collaborating with the Jülich Supercomputing Center, the applicant will join current efforts in bringing population neuroimaging analytics to high-performance computing clusters for efficient and novel analyses approaches. The applicant will be involved in the supervision and mentoring of PhD, MD, master and bachelor students.
Funding for the position is available for two years. Applications will continue to be received until the position is filled.
Please send your comprehensive application, including CV, publication record, copies of relevant certificates and either reference letters or names of two potential references to Prof. Dr. Svenja Caspers (s.caspers@fz-juelich.de mailto:s.caspers@fz-juelich.de).