Postdoctoral Fellow in Computer Science – Epigenomics – Data Science
The Waterland laboratory at Baylor College of Medicine (BCM) is making a difference by documenting, at the molecular level, the long-term adverse consequences of poor nutrition during development. Our overarching goal is to provide evidence to individuals and policy makers to improve nutrition of pregnant women, infants, and children worldwide. Join our team! We currently have multiple NIH-funded openings for highly motivated individuals interested in genomic and epigenomic data science.
The fellows will lead the design and implementation of data analysis projects aimed at understanding how epigenetic development affects risk of disease. Specifically, projects focus on the role of epigenetic dysregulation in cancer and obesity in humans, and mouse models of developmental epigenetics in the brain, related to energy balance and obesity. We are world leaders in understanding how DNA sequence encodes stochastic epigenetic variation (metastable epialleles). For examples see our recent papers in Genome Biology https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1708-1 and Nature Communications https://www.nature.com/articles/s41467-019-13339-3. All projects involve innovative analytical approaches to high-volume genomic, epigenomic, and gene expression data (next-generation sequencing – Bisulfite-seq, RNA-seq, etc.). The successful candidate will work independently and take a leading role in data analysis, interpretation, and visualization, and drafting of manuscripts for publication. For more information see https://www.bcm.edu/research/labs-and-centers/faculty-labs/robert-waterland-lab.
We are dedicated to advancing the careers of our trainees, and offer a competitive salary and stimulating work environment with opportunities for advancement. BCM is in the Texas Medical Center, the world’s largest medical center and a vibrant and collaborative research environment. Houston is a large, culturally diverse and thriving metropolis with a relatively low cost of living.
Recent PhDs with strong publications in computer science, bioinformatics, computational biology, and related fields will be considered. Programming experience using languages such as Python, R, and/or Perl is required. Experience in high-throughput analysis of next-generation sequencing data, programming experience in a UNIX/Linux environment, training in statistics, and familiarity with cluster computing and widely-used genomic databases and software are advantageous. Individuals already in the US are particularly encouraged to apply. We will also consider outstanding individuals at career stages other than postdoctoral, with appropriate experience.
No biological background is required, but the ideal candidate will have some familiarity with genetics and genomics. This position presents an outstanding training experience for someone with a computer science background who wishes to break into bioinformatics. Alternatively, this could be a great opportunity for someone with a strong background in molecular biology/genetics who seeks advanced training in computer science/data science. The candidate must have excellent verbal and written communication skills – in English – and be capable of working within a team of biologists and programmers.
To apply, submit a single PDF containing your CV, statement of research interests, and names and contact information of three references to: Robert A. Waterland, PhD, Baylor College of Medicine; E-mail: email@example.com .
Baylor College of Medicine is an equal opportunity / affirmative action / equal access employer.
Key words: big data, bioinformatics, cancer, computational biology, computer science, computer scientist, data analysis, data mining, data science, data visualization, development, developmental origins, developmental programming, DNA methylation, DOHaD, epigenetic, epigenetics, epigenomic, epigenomics, informatics, machine learning, metastable epiallele, next-generation sequencing, obesity, programmer, programming, software engineer
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