The Raj laboratory in the Departments of Neuroscience and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai (ISMMS) in New York City is looking for multiple motivated researchers to join our team as Postdoctoral Scholars to work on NIH-funded research in computational genomics. We have openings to work on projects using advanced computational and machine learning approaches to identify novel drivers of neurodegenerative disease pathogenesis. In particular, the successful candidates will work closely with Dr. David Knowles lab of the New York Genome Center and Columbia University to develop machine learning methods to enable improved understanding of the role of non-coding regulatory variation in Alzheimer’s disease. A major component of this work will rely on applying machine learning methods to whole genome sequence data (generated as part of the Alzheimer’s Disease Sequencing Project, ADSP), with the aim of identifying genetic variants that cause or contribute to the risk of or protection against the development of neurodegenerative disease.
The successful candidates will work closely under the mentorship of Drs. Raj and Knowles. They will join a multidisciplinary and highly collaborative research team that includes geneticists, computational biologists, neuroscientists, and physician-scientists at Icahn School of Medicine at Mount Sinai and New York Genome Center, as well as at other leading academic institutions.
The Raj lab has expertise in neurodegenerative disease genetics and functional genomics (eQTL) including experience with working with existing cohort data (AMP-AD, AMP-PD, ALS Consortium, CommonMind, etc.) and has active collaboration with clinicians to lead projects to study the role of monocytes and microglia in Alzheimer’s and Parkinson’s disease.
The Knowles lab has an active program in the development of novel machine learning methods and their application to data analysis challenges in genomics with the aim to better understand the role of transcriptomic dysregulation across the spectrum from rare to common genetic disease.
· Maintain and analyze in-house and external genomics/transcriptomics/proteomics data from disease cohorts
· Develop and implement algorithms, computational pipelines, statistical methods and software to analyze large-scale human sequencing data and perform integrative analysis
· Deploy deep learning models in cloud computing environment
· Publish and present novel research findings in academic journals and conferences
· Actively collaborate with other members of the research team
· Candidates with a doctoral degree in human genetics, quantitative genetics, bioinformatics, computational biology, computer science or other fields with strong quantitative skills will be considered
· Experience analyzing large biological datasets, including principled application of statistical techniques
· Expertise in Linux and high performance or cloud (e.g., AWS or GCP) computing environments
· Demonstrated experience in machine learning or applied statistical modeling
· Proficiency in Python and R
· Desire and ability to learn quickly, think creatively, and apply and design new analytic approaches
· Desire and ability to work both collaboratively and independently
· Effective oral, written, and interpersonal communication skills
Qualified applicants should send curriculum vitae, a brief statement of research interests, and contact details for three references to Towfique Raj at firstname.lastname@example.org.
Tagged as: Computer Science, Life Sciences, Mathematics
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