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Postdoctoral Fellow – Mathematical Oncology at City of Hope

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Employer: City of Hope

Expires: 08/02/2020

The Beckman Research Institute of City of Hope is looking for a talented Postdoctoral Fellow in the Division of Mathematical Oncology. A Postdoctoral fellow (also known as a postdoc) is an individual holding a doctoral degree who is engaged in a temporary period of mentored research and scholarly training for the purpose of acquiring the professional skills needed for their career development.  Established in 2015, the Division of Mathematical Oncology seeks to translate mathematics, physics and evolution-based research to clinical care. The Division is committed to both developing and applying novel technics of mathematics and physics to translational problems in oncology. To this end, the candidate should be dedicated to multidisciplinary research and demonstrate a track record of publications involving collaborative research in mathematics, physics, engineering, oncology or a closely related field. The candidate must have excellent communication skills. With guidance and mentorship from the Director of Mathematical Oncology, the candidate will be expected to work independently and support research into information flow and state-transitions in multiple omics datasets to study initiation, progression, and response to treatment in acute myeloid leukemia. Analysis will include but is not limited to: genomic analysis, time-series analysis, stochastic differential equation modeling, partial differential equation modeling, basic bioinformatics, and state-transition modeling. This position is supported by a U01 project grant through the Physical Sciences in Oncology Network (PSON) at the National Cancer Institute ( The candidate will be expected to lead authorship of manuscripts related to research and grant milestones and to provide material for grant progress reports.The candidate will be expected to design and execute research in the area of Mathematical Oncology including, but not limited to, the following domains:Cancer modelingNovel metrics of response to therapyCancer-immune system interactionsDynamical systems analysisState-transition modelingOrdinary, Stochastic, and Partial differential equation modeling (ODE,SDE,PDE)Data analysis Dimension reduction methods (PCA, t-SNE, UMAP, MDS,..)Key Responsibilities include:Prepare manuscripts and grant applicationsPresent research results at national and international meetingsReport research progress on a regular basisWith guidance and mentorship from the Director of Mathematical Oncology, develop independent research and apply for funding to support research projects.Develop novel mathematical models and/or quantitative analyses to interrogate experimental data produced by lab-bench and clinical oncology collaboratorsDevelop novel algorithms to identify and predict biological dynamicsUtilize data visualization to explore and communicate complex ideas, including nonlinear dynamics and theoretical concepts as they pertain to oncology researchSingle out and implement preexisting algorithms for the purpose of biomarker discovery and sample classificationAnalyze and model structured data using advanced mathematical methods and implement algorithms and software needed to perform analysesPerform machine learning, and statistical analysis methods, such as classification, time-series analysis, regression, and hypothesis validation methodsPerform explanatory data analyses, generate and test working hypotheses, prepare and analyze historical data and identify patterns  Basic education, experience and skills required for consideration:Ph.D. in applied mathematics, physics, oncology, or computer science or closely related field is requiredPreferred education experience and skills:Proficiency with at least one of the following programming languages: R, Python, MATLAB, and experience with databases and SQL highly desirableExperience with analyzing high throughput data such as transcriptomic, proteomic, and/or genomic data including bulk and single-cell datasetsExperience with time-series analysis and state-transition modeling