Job Description:
Job Description Overview: The Getz lab is looking for a highly motivated and talented individual with computational background to join the CGA Cancer Resistance team. The lab has undertaken an ambitious 5-year project aimed at discovering the basis of drug resistance in cancer. While cancer treatments (including chemo- targeted- and immuno-therapies) work for months or years cancers eventually recur. In most cancers and treatments the causes of drug resistance are not yet understood. About the job: As a member or this team you will collaborate with scientists and engineers in a collegial work environment with intellectual rigor. By applying your statistical and machine learning skills to multimodal cancer data you will drive the generation and testing of hypotheses leading to new biological insights in cancer resistance. This project is a rare opportunity to contribute to one of the most important and challenging problems facing cancer medicine today. There are a range of questions at the core of resistance: (i) What are the drivers of resistance (genetic and/or epigenetic) (ii) What are vulnerabilities of resistant cells. Novel therapies may exploit these vulnerabilities. We look for vulnerabilities using CRISPR gene knockout and small molecule screens of resistant and sensitive cell lines. (iii) Are there mutational mechanisms that contribute to development of resistance (e.g. APOBEC or MSI) (iv) What can we learn from the dynamics of clones under drug selective pressure? Can we usesub-clonal structure and dynamics to identify multiple resistance mechanisms in a single patient (v) Can we predict resistance mechanisms that will develop based on tumor type treatment genomics of tumor and patient i.e. can we find biomarkers of the resistance mechanism (vi) In which cases can we predict effective therapy combinations based on the initial biopsy We plan to characterize the mechanisms of resistance through genetic characterization of patient tumor samples (germline pre-treatment and post-resistance) and functional genomics using drug and CRISPR screens of cell lines. Characteristic Duties Design and execute data analysis strategies to support research projects involving multimodal cancer datasets. Together with other team members you will develop new methodologies for integrative analysis of functional and genomic data. Explore novel data representation modes with emphasis on integrating diverse data types. Analyses typically consist of the application or development of computational tools to a) assess data quality b) characterize germline and somatic variants and c) quantify how these variants impact tumor evolution and resistance to therapy. Present ideas and results to the multi-disciplinary members of the Cancer Genome Analysis Group. Prepare written reports and presentations for internal use as well as presentations at conferences. Qualifications A PhD in Computer Science (Machine Learning) Physics Statistics Math Engineering or a related quantitative discipline is required. Experience with computational analysis algorithm development statistics and machine learning. Proficiency in at least one modern programming language. Experience with a scientific programming environment such as python R or Matlab is preferred. Strong communication skills. Background in genetics or biology is a plus. Knowledge of cancer genomics is a plus but is NOT required "All computational scientists at Broad are encouraged to continue developing their expertise by engaging with the wider computational community through Broad's vibrant Models Inference & Algorithms Initiative (broadinstitute.org/mia)." All Broad employees regardless of work location must be fully vaccinated for COVID-19 by Tuesday October 12 2021. Requests for exemption for medical or sincerely held religious beliefs will be considered #LI-POST All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation gender identity national origin disability or protected veteran status. EEO is The Law - click here for more information Equal Opportunity Employer Minorities/Women/Protected Veterans/Disabled Check out this video for a look into our community! |
