Baylor College of Medicine, located in Houston, Texas, is a top-20 medical school ranked #1 in the state, a leader in genomics and epigenomics, and a pioneer in the field of environmental epigenomics. Baylor provides a rich scientific environment with many unique and unparalleled opportunities for collaborative research across the Texas Medical Center, the largest medical center in the world.
Baylor College of Medicine has an opportunity for a jointly mentored Postdoctoral position for training in integrative analysis of epigenomic, transcriptomic, metabolomics and proteomic large-scale datasets.
Dr. Cristian Coarfa is looking for highly motivated individuals to be cross-trained in multi-omics analysis and data science to conduct research aimed at understanding how exposures to environmental chemicals reprogram the transcriptome, epigenome, microbiome, and metabolome to cause disease (see Trevino et al, Nature Communication 2020, Katz et al, Environmental Health Perspectives 2020,, Suter et al, Biochem Biophys Res Commun 2019, Wang et al Nature Biotechnology 2018).
The position is intended for Computer Science or Computational Biology graduates transitioning to a data science and bioinformatics career. The successful candidate will receive training in analyses of multi-omics data and be expected to participate in multiple projects involving design, implementation and integration of large-scale datasets and data processing pipelines. The candidate will also perform advanced modeling of disease risk based on environmental exposures such as Polycyclic Aromatic Hydrocarbons (PAHs), organotin, and on multi-omics molecular responses, using approaches including generalized linear models and deep learning. Experience in epigenetics or gene regulation is a plus, and experience with statistical analysis tools such as R or Python is recommended (successful candidates will be expected to pass a basic programming test in Python). Due to the nature of the position, successful candidates must have excellent written and verbal English skills, strong communication and interpersonal skills, and be capable of working with large collaborative teams.
- Participate in multiple projects involving design, implementation and integration of large-scale datasets and data processing pipelines.
- Use statistical analysis tools such as R and Python.
- Perform advanced modeling of disease risk based on
environmental exposures such as Polycyclic Aromatic Hydrocarbons
(PAHs), organotin, and on multi-omics molecular responses, using
approaches including generalized linear models and deep
- Education Required: MD or Ph.D in Basic Science, Health Science, or a related field.
- Experience Required: None Required.
- Certification/Licenses/Registration: None Required.
- Ph.D. in Computer Science or Bioinformatics, or a PhD in biology and at least two years’ experience in data analysis, as well as a strong background in statistics, and familiarity with large data sets such as proteomics or sequencing.
Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.
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