Postdoctoral Research Associate

Imagen de Paola Giusti-Rodriguez


Fecha Límite: 

Domingo, 16 agosto 2020

Mi grupo de investigación está reclutando para varias posiciones para postdocs. Favor de contactarme directamente si tienen preguntas giusti [at]

Position description: We invite applications for a post-doctoral scholar to conduct cutting-edge laboratory, statistical, or bioinformatic analyses of genomic, transcriptomic, and epigenomic data. All projects are funded. We focus particularly on schizophrenia, major depression, and eating disorders. We are deeply collaborative and actively work with many major genetic, psychiatric, neuroscience, and functional genomic groups in the US, Scandinavia, Europe, India, and Australia. We have multiple large new datasets.

Given all the complexities of 2020, it is hazardous to define any position narrowly and we are thus open to a relative broad range of applicants with a mix of dry and wet bench training. For most, one type of training will predominate. These may be more on the dry lab/analytical side or more on the wet bench/experimental side. In both instances, an “omic” slant is essential along with a focus on the function of the brain (e.g., transcriptomics or epigenomics in neural tissue, increasingly at single cell level). Past successful Sullivan lab members have included statistical geneticists, bioinformaticians, as well as neuroscientists and epigenomicists. For the right applicant, we would craft a multi-year plan to maximize scientific output and the chances of obtaining grant funding and progression to an independent career.

Educational requirements: Ph.D. in genetics, statistics/biostatistics, genetics, or neuroscience

Qualifications and experience: Substantial experience in analysis of multiple types of modern datasets (e.g., genome-wide association, exome sequencing, whole genome sequencing, RNA-sequencing, ATAC-seq, Hi-C, ChIP-seq, etc.). Good programming skills are essential (e.g., R, Python) in order to flexibly manipulate large datasets along with the use of standard pipelines for analysis, bioinformatic integration, and gene set analyses.



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