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Postdoctoral Scholar - Research Associate

The Bridge Institute Los Angeles, California


The Kuhn-Hicks laboratory at USC is a leader in mathematical oncology for predictive mapping of cancer progression and treatment response. The laboratory uses clinical, demographic, and single cell morpho-proteo-genomic data to build predictive models to aid in improving patient care and outcomes.

Our rapidly growing team is looking for a postdoctoral fellow with expertise in data science to apply machine learning concepts to analyze clinical, liquid biopsy, and real-world evidence data sets. A successful candidate will have the determination to improve outcomes for cancer patients and discover novels ways of applying data simulation, machine learning, computational and analytical skills to complex data sets.

Job Responsibilities • Research, design, implement and evaluate machine learning algorithms and statistical models for clinical datasets • Identify technical challenges, define requirements and prioritize efforts to meet deadlines of internal team and external collaborators • Assist with defining requirements and architectures for next-generation machine learning / statistical analysis products • Develop model and algorithms, perform exploratory research, and collaborate with engineers as well as fellow data scientists to implement your solutions as products. • Apply machine learning to build prediction model for cancer treatment and side effects Education Ph.D. in Computer Science, Engineering, Mathematics, or equivalent. Skills and Expertise • Minimum 5 years of programming experience • Experience in the field of data science/data analytics, mathematics, or bioinformatics • Strong written and verbal communication skills • Experience in building models and developing algorithms for machine learning, statistics, optimization, and/or simulation • Experience in SQL, Python, and R Work Environment Work is in a typical office environment. Contact Applicants should include a cover letter as a part of the application. Contact Prof. Peter Kuhn at

REQ20071584 Posted Date: 03/13/2019
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