The University of Texas MD Anderson Cancer Center aims to eliminate cancer in Texas, the nation, and the world, through outstanding programs that integrate patient care, research, and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees, and the public.
MD Anderson Therapeutics Discovery Division
Within The University of Texas MD Anderson Cancer Center lies the Therapeutics Discovery Division (TDD), a powerful engine driving the future of new targeted, immune- and cell-based therapies. Therapeutics Discovery eliminates the bottlenecks that hamper traditional drug discovery by employing a multidisciplinary team of dedicated researchers, doctors, drug developers, and scientific experts working together to develop small-molecule drugs, biologics, and cellular therapies. Our unique structure and collaborative approach allow the team to work with agility, bringing novel medicines from concept to clinic quickly and efficiently – all under the same roof.
The TRACTION platform
The Translational Research to AdvanCe Therapeutics and Innovation in ONcology (TRACTION) platform is an industrialized translational research group that aligns world-class drug discovery and development with highly innovative the science and clinical care research, for which MD Anderson Cancer Center is known. Through an investment in patient-centric research, we have developed the infrastructure, platforms, and capabilities to enable transformative research. TRACTION’s approach combines innovative cancer genetics, disruptive technologies, deep mechanistic biology, disease modeling, and pharmacology to accelerate the translation of novel discoveries into definitive clinical hypotheses. By partnering with the drug discovery engines within Therapeutics Discovery, we aim to advance a portfolio of small molecules, biologics, and cell therapies for our patients. We work in a fast-paced, milestone-driven environment with a focus on team science and interdisciplinary research. Our unique approach has created a biotech-like engine within the walls of the nation’s leading cancer center to bring life-saving medicines to our patients more quickly and effectively.
We are seeking an a highly motivated and collaborative individual to join our team as an Institute Research Investigator (IRI) of Computational Biology. With strong expertise in computational biology, the Institute Research Investigator (IRI) be responsible for developing data analytics and data integration for team oriented projects that span early discovery to late stage translation, combining computational modeling with quantitative experimental data to understand complex biological systems and translate this understanding to the development of oncology therapeutics. These efforts will allow us to advance novel therapeutics currently under development by our Therapeutics Discovery teams and partners.
As a part of the Therapeutics Discovery team, you have the opportunity to use your talents to make a direct impact on the lives of our patients. Ideal candidates will have solid computer science and/or engineering/biostatistics obtained from an internship or work experience in addition to required education. Success will be measured by the ability to propose and execute computational biology workplans, integrating data from multiple molecular profiling platforms, and working closely with biologist to uncover biological and mechanistic insights, and to enable hypothesis-driven testing of oncology therapeutics and/or biomarker strategies in the clinical setting.
Ph.D. in Computer Science, Engineering, Applied Mathematics, Biostatistics or a related discipline from an accredited university
Preferred Candidate will possess the following:
1. Strong foundation in both computer science concepts and molecular / cancer biology.
2. Evidence of proficiency in programing languages (Python, R), scripting languages (bash), high-performance computing, code version control, and computational notebook tools.
3. Experience with cloud computing, container images, reproducible workflows, and interactive visualization.
4. Experience with machine-learning and/or data mining algorithms (ie. Clustering, classification, etc.), and experience utilizing common parametric and non-parametric statistical tests (ie. T-test, ANOVA, Wilcoxon- signed-rank test, Fisher’s exact test, etc.) for data analysis.
5. Evidence of ability to develop statistical algorithms, or the comprehensive assessment of algorithms, for the analysis of large multidimensional datasets of successfully manipulating large volume datasets and experience with high performance computing are essential
6. Knowledge and experience in areas of genomics, next-gen sequencing analytics (alignment tools, mutational variant callers, ChIP-seq, etc), pathway analysis, and network analysis.
7. Outstanding organizational skills and the ability to effectively present results and conclusions to co-workers, collaborators and manager.
8. Experience working with bench biologists, with examples where analytical methods enabled the validation of hypothesis.