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Institute Associate Scientist III - Computational Biology

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Research
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125266 Requisition #
Thanks for your interest in the Institute Associate Scientist III - Computational Biology position. Unfortunately this position has been closed but you can search our 471 open jobs by clicking here.
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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