Associate Scientist, Post Doctoral Fellow- Epstein-barr Virus & Multiple Sclerosis - Hybrid

Merck Merck · Pharma · PA

This postdoctoral research fellow position focuses on studying the link between Epstein Barr Virus (EBV) and Multiple Sclerosis (MS) using advanced analytical methods, including machine learning and omics approaches, to identify patterns in host immune response and potential biomarkers. The role involves developing statistical models, analyzing large datasets, and collaborating with a multidisciplinary team.

What you'd actually do

  1. Develop statistical models of host and immune response variables predictive of multiple sclerosis (MS) in matched case-control study.
  2. Identify and implement analytic methods (such as machine learning or group-based trajectory models) appropriate for large datasets (>6,000 variables) for exploratory analyses of serology data.
  3. Explore external datasets in which findings could be replicated for purposes of validation.
  4. Collaborate with both internal and external scientists to further the study of the role of immune responses in the relationship of Epstein Barr Virus (EBV) and multiple sclerosis (MS).
  5. Evaluate internal and external data sources related to characterizing the burden of multiple sclerosis (MS) as well as other autoimmune diseases and their relationship with Epstein Barr Virus (EBV)

Skills

Required

  • PhD within 6 months of hire in epidemiology and/or biostatistics; infectious diseases or related field.
  • Interest in the study of infectious disease and vaccines.
  • Experience in identifying and implementing appropriate statistical models.
  • Experience with conducting analyses using software such as R, SAS, or Stata.
  • Proficiency in written and verbal communication.

Nice to have

  • Knowledge related to immunology, pharmacoepidemiology, neurology, and/or oncology.
  • Skills and knowledge related to advanced analytic methods such as artificial intelligence and machine learning.
  • Experience conducting molecular epidemiology studies, including the analysis of serology samples and omics analyses.
  • Experience using large real-world healthcare data sources (e.g., insurance/administrative claims data)

What the JD emphasized

  • machine learning
  • advanced analytic methods

Other signals

  • machine learning
  • advanced analytic methods
  • identify patterns in host immune response
  • identify biomarkers