Post-Doctoral Fellow in Cancer Data Science
Post-Doctoral Fellow in Cancer Data Science
University of California, Los Angeles
Requisition Number: JPF04879
Open date: August 14th, 2019
Next review date: Saturday, Dec 28, 2019 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.
Final date: Tuesday, Jun 30, 2020 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
The newly formed Cancer Data Sciences group at the UCLA David Geffen School of Medicine and UCLA Jonsson Cancer Centre is seeking a Post-Doctoral Fellow in Cancer Data Science: Algorithm Development. The successful candidate will have a PhD in computational biology, statistics, computer science or an equivalent quantitative discipline. They will be working with a broad team of quantitative analysts, including Statisticians, Data Scientists, Clinical and Basic Science trainees, and a broad range of collaborators. They will drive the development and application of new quantitative strategies to improve our understanding and ability to treat cancer, working with cutting edge molecular and imaging datasets. They will be passionate about generating high-quality results, linking development of new methods directly to cutting-edge datasets ranging from DNA, RNA and protein sequencing to clinical data, digital pathology and radiomics. They will have a track-record of success with peer-reviewed research demonstrating a careful approach that identifies potential confounders and sources of bias, and reproducible analyses with high-quality source-code. They will apply their skills to projects that will be customized to the technical, personal and career aspirations of the candidate, but can range from biomarker development to new technologies to systems biology to machine-learning. Our team works with a broad range of approaches ranging from convolutional neural networks through to statistical modeling and systems biology approaches. The methods developed will be executed at scale in robust computational pipelines to analyze patient cohorts ranging from tens to hundreds of thousands of individuals on both local HPC and cloud-based computing environments. This will entail working closely with others, including Bioinformaticians to ensure reproducibility and generalizability. Our datasets comprise petabytes, and are growing rapidly, linked to key clinical endpoints and covering almost all tumour types. While a strong background in biology or cancer biology is beneficial, for this role very strong quantitative skills are essential. Strong scientific communication skills, orally and in writing as well as in designing high-quality scientific data-visualizations, are very valuable, and significant training and support will be given in this area. The successful candidate will be helping us perform research that will transform the lives of cancer patients.
Your responsibilities will be to use your biological and data science skills to analyze large datasets, including identification of key features using established or new pipelines, statistical and machine-learning analyses, data visualization, and written & oral reporting to translational, biologic, translational and clinical teams. Your work may focus on a single tumour type, or may cover a broad range, focusing on a subset of data types. You may work with molecular (WGS, panel-sequencing, RNA-Seq, proteomic) and/or imaging (digital pathology, radiomic) data, and practical experience in one of these two areas is a major asset. You will typically have one to two major and potentially some minor projects at any point in time. We are in a rapid growth-phase, and the successful candidate will be involved in hiring of new team members, and supporting their training and on-boarding. Beyond your strong inter-personal skills and computer science background, you will have experience with implementation skills at least one of C/C++, R, Perl or Python. You will be comfortable in UNIX/Linux environments and producing well-documented code. A core background in statistics is key, and supplementation with advanced understanding of time-to-event analyses, Bayesian statistics or machine-learning is beneficial. Experience with cloud-computing or HPC is a major asset. Recent publications from previous trainees in this team have included:
- Sinha A et al. (2019) “The Proteogenomic Landscape of Curable Prostate Cancer” Cancer Cell (PMID: 30889379)
- Chen S et al. (2019) “Widespread and Functional RNA Circularization in Localized Prostate Cancer” Cell (PMID: 30735634)
- Bhandari V et al. (2019) “Molecular Landmarks of Tumour Hypoxia” Nature Genetics (PMID: 30643250)
- Haider S et al. (2018) “Pathway-Based Biomarkers Enable Cross-Disease Biomarker Discovery” Nature Communications (PMID: 30420699)
- Espiritu SMG et al. (2018) “The Evolutionary Landscape of Localized Prostate Cancers Drives Clinical Aggression” Cell (PMID: 29681457)
The University of California is an Equal Opportunity/Affirmative
Action Employer. All qualified applicants will receive consideration
for employment without regard to race, color, religion, sex, sexual
orientation, gender identity, national origin, disability, age or
protected veteran status. For the complete University of California
nondiscrimination and affirmative action policy, see :UC
Nondiscrimination & Affirmative Action Policy.
UCLA, Westwood, CA
To apply, please visit: https://recruit.apo.ucla.edu/JPF04879
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status. For the complete University of California nondiscrimination and affirmative action policy, see: UC Nondiscrimination & Affirmative Action Policy, https://policy.ucop.edu/doc/4000376/DiscHarassAffirmAction