Computational Biologist at the USC Center for Applied Molecular Medicine
USC Center for Applied Molecular Medicine
Los Angeles, CA
Areas of Expertise Desired:
Our focus in the Center for Applied Molecular Medicine is the discovery of biomarkers that are indicative of a patient’s likely response to existing therapies. The benefits of a technology to “personalize” medical treatment by accurately predicting outcomes are hard to overestimate in terms of improved outcomes and reduced costs in bringing drugs to market and delivering effective care. The barriers to personalized medicine are both technological and biological in nature. First, reliable approaches to routinely and rigorously measure the composition of biological samples must be developed, then approaches for interpreting those measurements to describe the state of a complex biological system are required.
We are seeking a talented candidate with a strong interest in computational systems biology and expertise in the statistical analysis of biological data. The candidate is expected to perform innovative research to address biological questions that will help doctors provide better care to patients. The successful applicant will be capable of designing experiments and systems biology analysis of sizeable data sets from proteomic, genomic, epigenomic, or other high-throughput molecular assays to investigate hypotheses related to the prediction of key events in cancer progression and response to therapy.
Position will differ dependent upon background. Candidates with previous PhD or post-doctoral experience may be applicable for a senior scientist position.
This project focuses on the discovery of clinical biomarkers for disease progression and outcome through a variety of measurement techniques and sample types. Multiple experimental modalities, such as gene and protein expression, will be integrated through bioinformatics and systems biology techniques. Close collaboration with clinical/laboratory personnel will yield data from cell lines, animal models, and clinical samples. Additionally, publicly available data resources may be employed. Discovered markers will be validated using independent sample sets.
Necessary skills include a Masters or PhD or equivalent in biostatistics, quantitative sciences (physics, or applied mathematics) or engineering (electrical engineering, computer science, industrial engineering), previous experience in the statistical analysis of molecular biological data, and preferably at least one publication in peer-reviewed journals or well-regarded conference proceedings. Experience in systems biology, design of experiments, or dynamical systems analysis is preferred. Extensive hands-on expertise with the R data analysis platform and fluency with statistical data analysis methods are required.
The successful new hire will be highly self-motivated and creative and able to work effectively in a multi-disciplinary team. Candidate must also enjoy working with experimental data, rather than simply abstract algorithmic development. A combination of theory and practice will be critical for success. In addition, the candidate will develop presentations and contribute to authorship of peer-reviewed publications and grants.
Key success factors in the performance of this position include a high level of attentiveness, the ability to collaborate closely with others from diverse disciplines, and a willingness to learn.
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