Postdoctoral Fellow - Public Health Research: Bioinformatics, Machine Learning & Statistical Methods

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City of Hope
Bradbury, CA
Sep 14, 2021
Sep 21, 2021
Organization Type
The Beckman Research Institute of City of Hope is looking for a talented Postdoctoral Fellow with specialization in at least two of the following domains: data science, bioinformatics, machine learning, sensor data, geospatial analytics, complex statistical methods (e.g., Bayesian modeling). The ideal candidate will also have domain knowledge in public health-related applications and will join our team in working on a diverse array of research projects. The research fellow will work with Dr. Marta Jankowska from the Population Sciences Division at the Beckman Research Institute at City of Hope.

The first primary project seeks to develop methods for understanding and predicting minute-level behaviors such as physical activity, sleep, and eating behaviors. We aim to relate these factors to metabolic diseases and cancer risk as influenced by environmental contextual factors such as green spaces, noise, or air pollution, and eventually develop GeoAI driven Just-In-Time Adaptive Interventions.

The second primary project is working toward multi-modal data fusion of omics, sensor, biological, and social determinants of health data. Additional projects focus on development of advanced methods for dynamic environmental exposure estimation using GPS and accelerometer sensors, development of novel environmental measures through use of machine learning as applied to satellite imagery and generating spatial data applications for integration of registry data with environmental factors.

The successful candidate will have freedom to explore their own research interests within the areas of mHealth, data fusion, novel machine learning applications, advanced data science applications to health, GIScience and public health, environmental exposure science, health behavioral science, and metabolic and cancer related diseases. Opportunities for the fellow to apply for independent funding will be available and encouraged. The position will include leading data analysis and manuscript writing in collaboration with the research team. Additional funding will be available for attending scientific conferences, workshops, and trainings.

Basic education, experience and skills required for consideration:

A Ph.D. degree in a quantitative field such as Environmental Health, GIScience, Computer Science, Epidemiology, Bioinformatics, Biostatistics or a related discipline is required.
Experience working with large and complex data sets.
Domain application in public health and/or geospatial data is desired.
Strong statistical and data science skills in a programming language such as R or Python.
Excellent scientific writing and strong oral communication skills.
The ability to work effectively, collaboratively, and collegially with colleagues from different disciplines.

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