Full-Time Tenure-track Assistant Professor in Computational Biology @ Portland, Oregon, United States
Oregon Health & Science University (OHSU), located in Portland, OR is seeking exceptional Assistant Professor tenure-track faculty candidates to create the foundation of a newly formed cross-departmental, multidisciplinary program in computational biology.
As biomedical research becomes an increasingly data-intensive science, success will require a goal-directed, team-oriented approach in which innovations and ideas developed by computational researchers working on related problems serve to build on and enhance the work of all team members. We seek faculty members with a strong commitment to such integrated team science. A successful faculty member will conduct original research that incorporates computational tools, data resources, and algorithmic innovations developed by colleagues, and extends them to novel application domains and/or methodological advancements, which in turn serve to enhance a shared code base.
OHSU is ranked as one of the nations top biomedical institutions in patient care, research, and education, with an annual operating budget of over $2.3 billion, over $340 million in annual grant funding, over 4,000 students and trainees, and nearly 1 million patient visits per year. Multiple departments rank among the top 5 in annual grant. OHSU is one of the nation’s fastest growing biomedical institutions, bolstered by long-term resource commitments, such as the $1 billion Phil and Penny Knight “challenge” to support research in OHSU’s Knight Cancer Institute, led by Dr. Brian Druker, who pioneered the concept of gene targeted therapy by demonstrating the efficacy of the revolutionary cancer drug Gleevec in chronic myelogenous leukemia.
OHSU provides substantial resources to enable leading computational research, including a world-class exascale super computing cluster, close interactions and resource commitments by leading technology companies such as Intel, tight integration with clinical and basic research programs across campus, graduate training programs in quantitative biosciences, biostatistics, biomedical informatics, bioinformatics and computational biology, and substantial long-term resource commitments, including a major computational focus within the investment plan for the $1 billion Knight cancer “challenge”.
Research in the computational biology program will focus on integrative analysis of high dimensional heterogeneous molecular, imaging, and clinical data to infer predictive models of biological phenotypes and functional interventions that induce desired phenotypic transitions. Candidates should have advanced training in machine learning or statistical techniques, such as probabilistic graphical models or Bayesian inference, with a track record of innovation in a biomedical application, as evidenced by a strong publication record and development of novel software tools or inference methodologies.
The computational biology program will support and develop innovative computational approaches to a range of programs in basic and translational research. We are seeking candidates with medically relevant research interests in: 1) genetics/genomics; 2) microbiology/immunology; 3) tumor host interactions; 4) cancer microenvironment; 5) cancer biology; 6) cardiovascular disease; or 7) translation of clinically relevant predictive models.
Applicants should have a Ph.D. in computer science, applied mathematics/statistics, computational biology, or relevant quantitative scientific discipline. M.D. or M.D./Ph.D. candidates with advanced computational training are also encouraged to apply. Work experience in a professional software development environment is a plus.
Applications should include a CV, research plan, and cover letter, which may be up to 4 pages. We encourage including the following in the cover letter:
- Provide links to open source code (e.g. through GitHub or other code repository) that you are most proud of and describe the relevance to advancing one of the research areas listed in item 3 below.
- Describe in 1 or more paragraphs how your personal philosophy or career goals relate to the values of team-oriented science described at the start of this document, with examples of how you have embodied these values in your career.
- Indicate one or more areas that is the best match with your research interest, out of: 1) genetics/genomics; 2) microbiology/immunology; 3) tumor host interactions; 4) cancer microenvironment; 5) cancer biology; 6) cardiovascular disease; or 7) translation of clinically relevant predictive models.