Postdoctoral Research Fellow – Spatial Transcriptomics Computational Biology

United States Massachusetts Boston


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.

What You'll Be Doing:

We are seeking a passionate computational biologist with expertise in analyzing single cell sequencing and/or spatial transcriptomics data to join the Genome Sciences team within the Department of Genetics & Pharmacogenomics (GpGx) at our site in Boston. Our mission is to develop high-throughput cutting-edge technologies to better understand disease mechanism and to identify and validate new targets and biomarkers. Our group utilizes innovative genetic screening (i.e., CRISPR) and multi-omics single cell approaches such as simultaneous RNA and protein profiling (i.e., REAP-seq), immune repertoire profiling, CROP-seq/Perturb-Seq, and Spatial Transcriptomics to impact all stages of the drug development pipeline from pre-clinical to clinical development. Genome Sciences collaborates closely with the discovery therapeutic areas (i.e., oncology, immunology, and neuroscience) to utilize these technologies to pursue key biological questions.

The main goal of this project is to evaluate the cutting-edge Visium Spatial Transcriptomics technology as a platform that could be regularly used in Neuroscience and Oncology to boost discovery and study mechanism of action (MoA) across programs to help prioritize targets and better understand disease mechanism. The postdoc would: (1) develop visualization tools to enable rapid technology development cycles, (2) develop bioinformatic pipelines for data integration with other omics data from the same sample such as imaging mass cytometry (IMC), MERFISH, and single cell REAP-seq; and (3) analyze tissue sections from AD brain and tumors from pre-clinical models and patient samples to better characterize cellular cross-talk, distribution in disease, and study MoA in relation to efficacy.

Responsibilities include but are not limited to:

Pioneering analytics capabilities for this novel technology that combines histology with transcriptomics to provide a map of gene expression across tissue sections,
Developing bioinformatic pipelines for analyzing spatial transcriptomics data and integrating with other omics data from the same samples such as single cell RNA-seq, REAP-seq, or imaging mass cytometry (IMC)
Developing interactive visualization tools to enable rapid technology development cycles and exploration of the data
Evaluate the potential of the Spatial Transcriptomics technology for wide use across therapeutic areas (i.e., Oncology and Neuroscience) for (1) studying mechanism of action (MoA) in pre-clinical models (2) identifying biomarkers tied to MoA and efficacy, (3) profiling patient samples to determine if in vivo/in vitro model systems accurately represent human disease, and (4) discovery of novel targets and biomarkers.
Analyze tissue sections from AD brain and tumors from pre-clinical models and patient samples to better characterize cellular cross-talk, distribution in disease, and study MoA in relation to efficacy.
Being proactive and collaborating closely across multi-disciplinary groups including, Oncology, Neuroscience, Research IT, Oncology Preclinical Analytics and Discovery PGx
Written and verbal communication of study results to project teams and leadership, including proposals for further experiments to validate key findings
Publish findings in high impact journals


What You Bring:

Candidates must currently hold a PhD or receive a PhD by summer 2020 in Bioinformatics, Biostatistics, Computational biology, Computer Science, Immunology, Molecular Biology, Statistics or related field
Demonstrated experience on the computational analysis and interpretation of single cell RNA-seq or and/or Total-Seq datasets
Expertise with data integration and interpretation
Proficiency in at least one programming language, such as R or Python
Familiarity with data visualization packages, such as R Shiny, ggplot, or plotly
Strong communication (oral and written) and collaboration, including an ability to effectively communicate experimental design, results and conclusions
Publications in high impact journal

Preferred Experience and Skills

Experience with high-performance Linux cluster and cloud computing
Experience with reproducible research tools and version control
Previous experience with statistical learning techniques is a plus

Start date

February 19, 2020

How to Apply

All candidates must apply directly online at