Human tumors are complex ecosystems involving cancer and immune cells as well as supporting tissues and vasculature in a complex three-dimensional tissue. The objective of this project is to apply methods developed for the analysis of geographic and ecological features in GIS (Geographic Information Systems) to the understanding of human cancers. This will involve applying geospatial analytics, spatial and spatiotemporal statistics, environmental image processing, and ecosystem modeling techniques to human tumor images collected using very recently developed methods. These images comprise 106 or more cells per image at sub-cellular resolution (ca. 200 nm) and 40 or more different “channels” representing the distributions of landmark proteins and genetic mutations. The study aims to characterize patterns, relationships, and interactions among tumor cells, immune cells, and healthy cells based on these landmarks before, during, and after exposure of patients to therapeutic drugs.
The position involves a joint appointment between the Harvard University Center for Geographic Analysis (CGA) and the Harvard Medical School Laboratory of Systems Pharmacology (LSP). The candidate will be jointly supervised by Dr. Wendy Guan (Executive Director of the CGA) and Professor Peter Sorger (Director of the LSP) with day-to-day guidance from Dr. Denis Schapiro, LSP Research Fellow. The candidate will have space assigned at both the CGA and LSP and will attend group meetings and similar activities at both research centers. The appointment is for one year with the possibility of renewal based on satisfactory performance and continued availability of funding.
A Doctoral degree in geographic information science, remote sensing or a related field. Interest in biological imaging is required.
Strong understanding of spatial statistics. Proficient with common GIS and remote sensing software packages. Experience with multispectral and hyperspectral image analysis technologies. Knowledgeable in geospatial analysis and ecosystem modeling practices. Must have superior technical skills, attention to detail, and the ability to function as a contributing team member. Must have strong documentation and communication skills. Must be able to collaborate effectively with researchers across organizational structures and across knowledge domains.
To apply, send a curriculum vitae, statement of research interests, and names and contact information for three references to Wendy Guan (firstname.lastname@example.org). Research papers (up to three) are welcome but optional. Review of applications will begin immediately and end when the position is filled. Incomplete applications will not be reviewed.
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Harvard University is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions, or any other characteristic protected by law.