Postdoctoral Research Fellow - Computational Biology

Employer
Pfizer
Location
Cambridge, Massachusetts, US
Salary
Competitive
Posted
Apr 28, 2021
Closes
May 07, 2021
Ref
2075991156
Discipline
Biology, Modeling
Employment Type
Fellowship
Salary Type
Salary
Role Summary

The Simulation and Modeling Sciences group is seeking an outstanding postdoctoral researcher to join us to help advance statistics and machine learning for computational biology in the area of gene therapies and other oligonucleotide-based modalities. We are looking for applicants with a research background in biostatistics and machine learning, familiarity with computational biology, and able to develop new algorithms for practical applications. We offer a supportive and collaborative environment for cutting-edge interdisciplinary research.

Role Responsibilities

Develop and apply statistical and machine learning algorithms for multi-omics high-throughput datasets including short and long-read sequencing data.
Write well-documented code individually and collaboratively within a high-performance scientific computing environment.
Effectively communicate the advantages and caveats of developed algorithms to internal audiences with diverse backgrounds.
Collaborate with computational and experimental scientists located at multiple locations.
Publish articles in top peer-reviewed journals and deliver scientific and technical presentations at internal and external venues.
Basic Qualifications

Ph.D. in biostatistics, computer sciences, or a related technical field.
Familiarity with next-generation sequencing technology.
Well-cited journal publications.
Programming experience in Python, R, or equivalent languages.
Strong oral and written communication skills to work in a team-based environment.
Preferred Qualifications

Research experience in the analysis of high-throughput sequencing data.
Advanced coursework in molecular biology.
Familiarity with gene therapies and other oligonucleotide-based modalities.
Experience with a cloud-based computational environment such as a SLURM based cluster

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