Postdoctoral Position on Imaging Genetics
Posted 20 Aug 2018
Please send a brief letter of motivation and your detailed CV as one pdf (<2MB) to firstname.lastname@example.org.
The successful applicant will work in the clinical research group on Alzheimer Precision Medicine (GRC-APM) at the site of Pitié-Salpêtrière Hospital of Sorbonne University under the supervision of Professor Harald Hampel, holder of AXA Research Fund & Sorbonne University Chair.
The GRC-APM's main objective is advancements in detecting, treating, and preventing Alzheimer’s disease (AD) by the implementation of a precision medicine (PM) strategy. The successful applicant will collaborate in the area of imaging genetics involving the development of algorithms for the identification of novel multimodal (imaging and genetic) feature combinations and probabilistic models that explain these imaging-genetic interactions and facilitate diagnoses and prognoses in cognitive intact older adults at risk for AD and AD dementia patients. The candidate will benefit from collaboration with multidisciplinary team members and from ongoing collaborations with European and U.S. research centres.
- To conduct analyses of neuroimaging and genetics data on large datasets.
- To develop statistical models and/or use softwares that incorporates information across different modalities of data such as imaging (3D-T1; rs-fMRI; DTI; amyloid-Pet and FDG-PET, genetic, gene expression, biofluids).
- To write scientific publications.
- To collaborate with other team research scientists.
- To present findings at international conferences.
The position is supported for four years. However, candidates will be appointed for one one year, with an extension possible based on progress.
The salary compensation is very competitive: €3,700 gross / month.
- Ph.D. in biostatistics, computer science, machine learning, electrical engineering, or related fields, with research experience in statistical machine learning using complex multivariate data.
- Experience on probabilistic graphical models, and/or dimensionality reduction, and and/or sparse learning is highly desirable. Researchers with image-processing pipelines and gene-expression analysis are particularly encouraged to apply. Strong programming skills (working knowledge of Linux, C/C++, Python, Matlab) is desirable
- Candidates are expected to have a strong background in machine learning, advanced statistical modeling and data analysis, and excellent writing and communication skills, as well as experience in biostatistics and particularly analyzing genetic and imaging data.