Postdoctoral Research Fellow - Data Science (Internal Medicine) (Finance)
What we do here changes the world. UTHealth Houston is Texas' resource for healthcare education, innovation, scientific discovery, and excellence in patient care. That's where you come in.
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- 100% paid medical premiums for our full-time employees
- Generous time off (holidays, preventative leave day, both vacation and sick time - all of which equates to around 37-38 days per year)
- The longer you stay, the more vacation you'll accrue!
- Longevity Pay (Monthly payments after two years of service)
- Build your future with our awesome retirement/pension plan!
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- Free financial and legal counseling
- Free mental health counseling services
- Gym membership discounts and access to wellness programs
- Other employee discounts including entertainment, car rentals, cell phones, etc.
- Resources for child and elder care
- Plus many more!
General Summary: The post-doctoral fellow will work independently in a multidisciplinary supervised environment on the large data derived from murine and patient derived samples to study pathogenesis of GI diseases including aging pathways and cancer. This position is responsible for the integrative analysis of high throughput genomic data, and works with the center investigators to usher in the era of precision medicine through understanding host-microbial interaction over time (circadian and longitudinal) to improve our understanding of disease pathogenesis and response to therapy.
The selected candidate has experience working with cell culture and cell treatments as well as with lab-based assays including protein and RNA based techniques. The post doc is expected to be able to draft results of the studies being generated on circadian and microbiota experiments.
Principal Duties and Responsibilities:
1. Interpret and prepare results on omics data (circadian and microbiome related).
2. Design, develop and execute computational research projects of high complexity.
3. Clustering techniques (e.g., C-means clustering) for high-dimensional datasets.
4. Interact cross-functionally with the center investigators and scientists.
5. Evaluate new emerging technologies in relevant fields.
Preferred Knowledge, Skills and Abilities:
• Degree in a quantitative discipline (e.g. physics, statistical genetics, cancer genetics, bioinformatics, computational biology, or similar). Alternately, a degree in molecular biology combined with a strong record of high-throughput sequencing data analysis.
• Significant quantitative training in physics, mathematics or probability and statistics.
• Experience working with large -omics data sets.
• Fluent in R, Python, or similar.
• Experience in creating interactive apps in Shiny (R) or Flask/Django (Python).
• Experience with Github or other version controlling and code distribution software.
• Experience working with Docker containers, cloud-based and/or HPC/GPU computing.
• Experience developing, training, and evaluating classical machine learning models.
• Demonstrated willingness to both teach others and learn new techniques.
• Knowledge with public/NIH databases such as GDC, GEO, dbGaP, etc.
• Experience creating and administrating databases.
• Experience in network analysis.
• Experience in handling data from RNA seq, ChIP seq, and Whole Exome Sequencing
Minimum Education:
Doctoral/Terminal Degree
Physical Requirements:
Exerts up to 20 pounds of force occasionally and/or up to 10 pounds frequently and/or a negligible amount constantly to move objects.
Security Sensitive:
This job class may contain positions that are security sensitive and thereby subject to the provisions of Texas Education Code § 51.215
Residency Requirement:
Employees must permanently reside and work in the State of Texas.
