Computational Biology Internship (Summer 2019)
Passionate about making a difference in the world of cancer genomics?
Tempus is on a mission to connect an entire ecosystem to redefine how genomic data is used in cancer clinical settings. With the advent of genomic sequencing, we can finally measure and process our genetic makeup. We now have more data than ever before but providers don't have the infrastructure or expertise to make sense of said data. Here at Tempus, we believe the greatest promise for the detection and treatment of cancer lies in the deep understanding of molecular activity for disease initiation, progression, and efficacious treatment based on the discovery of unique biomarkers.
We are seeking an independent and motivated Computational Biologist Intern to work with our Computational Immunology group. This individual will work in an interdisciplinary team to study the tumor-immune microenvironment using unique and growing collections of genomic data coupled with clinical data. The successful candidate will work in a team, carry out data analysis, and apply best-in-class algorithms - or develop new algorithms - that directly address important biological and clinical questions.
What You’ll Do
- Design, develop and execute computational research projects of high complexity.
- Evaluate new emerging technologies in relevant fields.
- Communicate highly technical results and methods clearly.
- Interact cross-functionally with a wide variety of people and teams.
- Working towards a master’s or higher degree in a quantitative discipline (e.g. statistics, bioinformatics, computational biology, computer science, applied mathematics, applied physics or similar). Alternately, working towards PhD in molecular biology, immunology, etc. combined with a very strong record of high-throughput data analysis, or equivalent practical experience.
- Fluent with R, Python, or similar.
- Previous experience extracting and cleaning large data sets
- Interest in immunology and immunotherapy
- Prior experience working in diagnostic technologies, human immunology, cancer biology, infectious disease, and/or high-throughput screening.
- Significant quantitative training in probability and statistics.
- Demonstrated willingness to both teach others and learn new techniques.
- Familiarity with TCGA, GTEx, IMGT, Immgen databases