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Postdoctoral Researcher, Computational Biology

Be a part of the legacy: Postdoctoral Research Fellow Program
Our Research Laboratories’ Postdoctoral Research Fellow Program aims to be a best-in-industry program for industrial postdoctoral researchers, designed to provide you with an academic focus in a commercial environment. With the resources, reach, and expertise of a large pharmaceutical company, postdoctoral researchers will be positioned to excel in an institution committed to breakthrough innovation in research and discovery.
The Data & Genome Sciences department is looking for a passionate and talented Postdoctoral Researcher - Computational Biologist to join our Computational Biology & Genomics research team based in South San Francisco, CA. Oncology research at our company is driven by a deep interest in the biology of tumor and its microenvironment, and how diverse points of intervention can be combined to achieve ever higher rates of durable response and patient overall survival. Towards this goal, the successful candidate will develop a novel target discovery framework that identifies rational pairs of collaterally sensitive drug combinations. They will advance our understanding of tumor heterogeneity under therapeutic pressure using machine learning and bioinformatics approaches to interrogate high-throughput multi-omics assays, including RNAseq, scRNAseq, MIX-seq, and large compound screens. This research project provides ample opportunity for collaboration with cross-functional teams of computational biologists, data scientists and colleagues in Discovery Research as well as Academic collaborators.
In this exciting role, you will:
  • Explore subclonal transcriptomic and genomic tumor diversity in cancer in response to cytotoxic therapeutic pressure to discover concurrently developing mechanisms of resistance on single cell level (MIX-seq)
  • Connect the resistance trajectory findings to the drug mechanisms of action to identify pairs of compounds that will independently confer maximum cytotoxic effect in heterogeneous tumors.
  • Develop a hypothesis-generating analytical framework that incorporates knowledge of genomics and transcriptomics profiles of targeted patient populations to provide predictive hypotheses for mechanisms of resistance and rational drug combinations.
  • Collaborate with experimental scientists across functions to characterize novel targets coming from genetics, translational and disease pathway exploration.
  • Work with large internal and public biological data sets including Next Generation Sequencing (NGS) data (e.g. RNA-Seq, single cell RNA-Seq, MIX-seq, WGS).
  • Be proactive and work collaboratively across disciplines, including molecular biologists, protein scientists, bioinformaticians, and software engineers.
  • Employ best reproducible research and data integrity practices to generate reusable analysis frameworks.
  • Attend and actively participate in department and group meetings, code review sessions, as well as internal and external scientific symposiums and meetings/seminars.
  • Conduct literature mining, review evaluate and present bioinformatics analysis methods pertaining to the research project.
  • Write and review publications and manuscripts.
Education Minimum Requirement: 
Ph.D. or will you have completed your PhD within the next year, in Bioinformatics, Biostatistics, Computational biology, Computer Science, Genetics, Mathematics, Molecular Biology, Statistics or related field.
Required Experience and Skills: 
  • Passion to solve biological problems and identify problems that can be efficiently solved through computational methods and algorithms.
  • Experience with computational analysis and biological interpretation of diverse large-scale NGS experimental datasets.
  • Proficiency in at least one statistical programming language, such as R or Python.
  • Interest in identifying novel applications of AI / machine learning strategies for biological target discovery.
  • Demonstrate the ability to learn, be proactive and motivated, and consistently focus on details and execution.
  • Excellent oral and written communication skills.
Preferred Experience and Skills:
  • Familiarity with public databases, and repositories of DNA, RNA, single cell profiling and functional genomics and compound screening data.
  • Skilled at integrating results generated from multiple omics data sources, and biological knowledge bases to customize analytical approaches for discovery research.
  • Previous experience with experimental design of biological assays, statistical hypothesis testing, and biological interpretation.
  • Experience applying AI / machine learning methods for analysis of image-based biological readouts.
  • Understanding the pros and cons of various algorithms for DNA-seq, RNA-seq, single-cell RNA-seq and/or functional genomics data.
  • Experience with version control environments, such as Git.
  • Experience with high-performance Linux cluster and cloud computing.