Postdoctoral Scientist, Bioinformatics

North Chicago, IL, US
Oct 07, 2019
Required Education
Position Type
Full time
The Genomics Research Center (GRC) is a center of excellence for genetics and genomics that supports both drug discovery and development at AbbVie. The GRC plays an integral role in understanding human disease biology, finding the right targets, and investigating response to our drugs in clinical trials. Within the GRC, the Computational Genomics group works closely with disease area experts to design studies, analyze genomic data, and communicate results. We have an exciting opportunity for a postdoctoral fellow in Computational Genomics, based in Lake County, IL.

The postdoc program offers a balance of structured mentoring and work experience to help participants learn about and contribute to the drug discovery and development process. It also provides a chance to establish working relationships with scientists and leaders across multi-disciplinary groups, including disease area experts in the relevant therapeutic area(s) (Immunology, Oncology, Neuroscience, Virology, and/or General Medicine). The postdoc will have access to a high-performance computing cluster and bioinformatics support. S/he will be encouraged to exercise creativity, innovative thinking, and calculated risk-taking in conducting research projects.

We are seeking a highly motivated postdoctoral fellow to be part of the GRC who will develop and apply computational and statistical methods for one of three potential projects described below. Project of choice will depend on candidate's experience and interest. Research results are expected to be published in high quality peer-reviewed scientific journals and presented at relevant scientific conferences.

Key Responsibilities:
  • Work closely with therapeutic areas to identify key biological questions, discuss progress and results, and give presentations.
  • Drive the development of a statistical framework for one of the following projects:
    1. Integrating function and genetic association for complex diseases
      • Develop statistical framework integrating functional evidence from epigenomic and evolutionary genomic datasets into rare variant testing
      • Explore genotype - risk - disease relationship within Mendelian Randomization framework using rich electronic health records
      • Apply framework to 500,000 restricted access whole exomes and deep phenotypes from the UK Biobank consortium as well as public genomic datasets
    2. Jointly analyzing multiple modalities of single cell data including gene expression, protein expression, and CRISPR perturbations
      • Develop a framework for joint learning of cell types from multi-omic single cell data using methods such as clustering and deep learning
      • Use quantitative modeling to infer relationships between the different modalities
      • Apply these approaches to the analysis of novel and publishable internal data sets
    3. Joint modeling of gene expression and methylation data
      • Leverage cell type deconvolution methods to create cell-type-specific gene expression and methylation models
      • Investigate causal relationships between gene expression and methylation using Mendelian Randomization or related frameworks; extend approach to investigate drug mechanism of action
      • Perform longitudinal analyses to identify or confirm potential causal relationships between expression and methylation

  • PhD in Computational Biology, Bioinformatics, Statistical Genetics, Genomics, or related field.
  • A high degree of statistical and computational competency, with experience applying these skills to problems in genomics, pharmacology, or other relevant areas (e.g. demonstrated with relevant publications).
  • Fluency in one or more relevant programming languages, such as R or Python.
  • Strong communication skills in a collaborative environment.
  • Should be able to provide three letters of recommendation, one at least from your PI, and preferably, one from the Chair of your department or a member of the thesis committee.

  • Evidence of independent research capability including hypothesis development, experimental design and execution, data interpretation, problem solving, and project management.
  • Experience in one or more of the above-listed project areas.
  • Strong track record of high-impact scientific achievement.

Key Leadership Competencies:
  • Builds strong relationships with peers and cross functionally with partners to enable higher performance.
  • Learns fast, grasps the "essence" and can change course quickly where indicated.
  • Raises the bar and is never satisfied with the status quo.
  • Creates a learning environment, open to suggestions and experimentation for improvement.
  • Embraces the ideas of others, nurtures innovation and manages innovation to reality.
  • Strong interpersonal and communication skills with demonstrated ability to work within a team environment.