Institute Associate Scientist III - Computational Biology
MD ANDERSON THERAPEUTICS DISCOVERY
Within The University of Texas MD Anderson Cancer Center lies a powerful engine driving the future of new targeted, immune- and cell-based therapies: the Therapeutics Discovery Division. Therapeutics Discovery eliminates the bottlenecks that hamper traditional drug discovery, with 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 Therapeutics Discovery Division is built around four platforms: The Institute for Applied Cancer Science (IACS), ORBIT (Oncology Research for Biologics and Immunotherapy Translation), TRACTION (Translational Research to Advance Therapeutics and Innovation in Oncology) and the Neurodegeneration Consortium.
TRACTION is the translational biology team within the Therapeutics Discovery Division. We employ disruptive technologies, innovative biomarker approaches, cutting-edge pre-clinical modeling and unparalleled access to patient data to accelerate drug development and inform innovative clinical trials. Through integration with basic and clinical research faculty across MD Anderson Cancer Center, we leverage a team science approach with unmatched focus on patient-centric research. In partnership with the drug discovery engines of Therapeutics Discovery, TRACTION scientists execute ground-breaking translational science in support of our mission to advance our portfolio of novel therapeutic concepts into transformative treatments.
As part of the TRACTION platform, the Associate Scientist III in Computational Biology will contribute analytical and statistical support on programs that span the drug discovery and development continuum from target identification through clinical development. The candidate will integrate computational modeling with quantitative experimental data to understand complex biological systems and translate this understanding to support oncology drug development. 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. We are seeking a highly motivated and collaborative individual to join our Therapeutics Discovery team. Ideal candidates will have solid computer science and/or engineering/biostatistics obtained from an internship or work experience in addition to required education.
KEY FUNCTIONS
1. Under minimal supervision, contribute to the development, validation, implementation and execution of computational biology data-analysis tools that support the discovery and validation of novel targets.
2. Works closely with research team to quantitatively interrogate hypotheses around tumor related genes/pathways to further advance scientific discovery and clinical therapeutic drug development.
3. Develop and validate novel analytical frameworks for integration of multi-dimensional biological datasets that enable rapid dissemination, interpretation and visualization of biological information by the program biology teams.
4. Work with biologists to design statistical powered experimental studies that will inform on target biology, pathway activity, biomarkers of response, and clinical development.
5. Perform common statistical analysis on biological datasets including parametric and non-parametric tests, data mining / machine learning algorithms.
6. Leverages oncogenomic datasets to identify targets, biomarkers of response, and develop clinical path hypothesis.
7. Develop workflows for the processing, management, and quality control tracking of large biological datasets.
8. Effectively communicate program progress/issues to colleagues and contribute to overall success of project implementation.
Education
Required: Bachelor's degree in Biology, Biochemistry, Molecular Biology, Cell Biology, Enzymology, Pharmacology, Chemistry or related field.
Preferred: Master's degree in Computer Science, Engineering, Applied Mathematics, Biostatistics or a related discipline related discipline from an accredited university.
Experience
Required: Three years of relevant research experience in lab. With preferred degree, one year of required experience.
Preferred: Strong foundation in both computer science concepts and molecular / cancer biology. Proficient in PERL/Python, UNIX, and statistical computing platforms (R, Matlab, etc). Experience manipulating large volume datasets and experience with high performance computing are essential. Familiar with appropriate data normalization techniques and analysis of batch effects. Previous hands-on experience working with computational and statistical tools for the analysis of biological datasets. Specifically, the applicant should have 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. These will techniques be used across all stages of drug discovery from target discovery, target validation, responder ID hypotheses generation, and biomarker discovery.
Development of statistical algorithms, or the comprehensive assessment of algorithms, for the analysis of large-scale biological datasets. Previous experience in an oncology research laboratory. Training in bench biology techniques and experimental design. Extensive experience collaborating with bench biologists, with examples where computational biology methods enabled the validation of hypothesis. Previous experience with next-gen sequencing analytics (alignment tools, mutational variant callers, ChIP-seq,etc). Experience with pathway analysis, network analysis, and transcriptional regulator networks.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html
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