Job ID:
Job date: 2018-04-21
End Date: 2018-06-20
Company : Broad Institute Country : United Kingdom Role : Other
Job date: 2018-04-21
End Date: 2018-06-20
Company : Broad Institute Country : United Kingdom Role : Other
Job Description:
We are looking for a highly motivated and talented individual with computational background to join the CGA Cancer Resistance team. We have 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. 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 use sub-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 characterise 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. 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. CHARACTERISTIC DUTIES:
Additional Info:
EOE / Minorities / Females / Protected Veterans / Disabilities [Click Here to Access the Original Job Post]
We are looking for a highly motivated and talented individual with computational background to join the CGA Cancer Resistance team. We have 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. 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 use sub-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 characterise 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. 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. 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-disiplinary members of the Cancer Genome Analysis Group. Prepare written reports and presentations for internal use as well as presentations at conferences.
- A PhD in Computer Science, Engineering, Math, Statistics, Physics, 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.
Requeriments :
Skills :
- Biology
- Genetics
- Machine Learning
- Programing in Python
- Programing Skills
- Programming in Matlab
- Programming in R
- Statistics
Areas :
Additional Info:
EOE / Minorities / Females / Protected Veterans / Disabilities [Click Here to Access the Original Job Post]