The Bonneau lab at the NYU Center for Data Science (CDS) is seeking a postdoctoral data science fellow to research and develop computational methods for integrating data to improve drug development, by collaborating with the Quantitative Sciences and Innovation group at Novartis Institutes for BioMedical Research (NIBR). The data sources are diverse and can comprise of Novartis proprietary data including DNA-seq, RNA-seq, SOMAscan, wearables, demographic and biomarker measures from clinical trials, as well as public data sources including UKbioank, GWAS, electronic health records, and more. This project will research computational methods for the purpose of re-positioning existing drugs, evaluating possible combination therapies, and identifying subgroups of patients likely to respond better to treatment. This position is a two-year appointment with competitive salary and may be renewed for a third year. The fellow will be based at NYU and expected to spend two months per year in Cambridge, MA (at NIBR, studio/1br apartment will be provided). This position is designed to poise the fellow for future success at both academia and industry, and the chance to learn what career path they want to pursue. Strong scientific publication(s) and conference presentations are an expected outcome. A positive impact on patient's well-being is the research goal.
Position title: Postdoctoral Fellow
Reports to: Dr. Richard Bonneau
Location: New York, New York
Appointment: Two-year appointment, may be renewed for a third year, contingent on funding
Start Date: ASAP (ideally Nov 1, 2018). Applications will be accepted until the position has been filled.
Requirements/Skills: 1.Doctoral degree in the area of computational biology, bioinformatics, systems biology or a related field. 2.Extensive knowledge of computational genomics and experience with high-throughput data analysis. 3.Experience in Linux/Unix cluster environment. 4.Experience with statistical programming in Python or R. 5.Strong background in algorithm development, statistical methods and machine learning. 6.Background in biological sciences. 7.Outstanding personal initiative and the ability to work effectively as part of a team. 8.Excellent verbal and written communication skills.
Application documents: (1) curriculum vitae, (2) brief cover letter (no more than one (1) page), (3) one sample publication or pre-print, and (4) and three references.
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