Job ID: 3286
Job date: 2015-04-28
End Date:
Company : Memorial Sloan Kettering Cancer Center Country : Role : Postdoc
Job date: 2015-04-28
End Date:
Company : Memorial Sloan Kettering Cancer Center Country : Role : Postdoc
Job Description:
IntroductionWe are seeking outstanding postdoctoral candidates with a background in computational biology, statistical / machine learning applications, or a related quantitative discipline with an interest in cancer genomics to work with a strong network of research and clinical investigators, as well as bench scientists in the lab on:
- Development of integrative modeling approaches for genotype-clinical correlation studies of large ( >1000/3000) well –annotated clinical trial cohorts. The applicant will work closely with the Center for Molecular Oncology (CMO) and the computational oncology service at Memorial Sloan Kettering Cancer Center (MSKCC) to analyze high-quality genome profiling data from diagnostic samples. The generated data will be used to drive the development of statistical models that integrate related and orthogonal datasets (genomic, transcriptome, demographic and clinical variables) to delineate refined ontologies in cancer and deliver comprehensive and personalized prognostication models that take into account the clinical pathophysiological parameters and individual genomic profiles of each sample.
- Studies of clonal heterogeneity. The candidate will apply comprehensive genome profiling approaches to clinical trial cohorts to study molecular and clonal responses to investigational therapeutic interventions. This will form the basis of integrative single-cell genome profiling studies of select patient populations to study profiles of therapeutic resistance and response. This role will apply statistical methodologies to support the generation of high-quality single-cell derived data as well as develop innovative statistical modeling approaches to interrogate the biological and clinical implications of the underlying genomic and clonal variation in cancer.
Responsibilities
- Ensure generation of high-quality genome profiling data and integration of large complex datasets for analysis.
- Develop innovative analytical methods to derive biological and clinical insights from complex genome profiling datasets.
- Develop ideas and drive own independent research, present findings in meetings, and submit research for peer review and publication.
- Work independently and as a team member contributing towards collaborative research initiatives in the laboratory.
Required Qualifications
- PhD in Computational Biology, Biostatistics, Mathematics or a related quantitative field (eg: Statistical Genomics, Physics, Computer Science, Bioinformatics, Statistics).
- Demonstrated experience in applying statistical learning approaches to the analysis of complex and heterogeneous datasets.
- Knowledge of NGS derived data types and related technologies.
- Proficient in at least one of R (preferred), MATLAB.
- Proficient in one or more programming languages (eg: Perl, Python, C)
- Good written and oral English communication skills
- Alternative career paths may be considered for candidates with work experience or equivalent qualifications.
Memorial Sloan Kettering Cancer Center is an equal opportunity employer with a strong commitment to enhancing the diversity of its faculty and staff. Women and applicants from diverse racial, ethnic and cultural backgrounds are encouraged to apply.
To learn more about our lab, please visit us: http://www.mskcc.org/research/lab/elli-papaemmanuil
Please mention [Indeed] in the subject of your emailed application, OR within the body of your email.
Additional Info:
[Click Here to Access the Original Job Post]
IntroductionWe are seeking outstanding postdoctoral candidates with a background in computational biology, statistical / machine learning applications, or a related quantitative discipline with an interest in cancer genomics to work with a strong network of research and clinical investigators, as well as bench scientists in the lab on:
- Development of integrative modeling approaches for genotype-clinical correlation studies of large ( >1000/3000) well –annotated clinical trial cohorts. The applicant will work closely with the Center for Molecular Oncology (CMO) and the computational oncology service at Memorial Sloan Kettering Cancer Center (MSKCC) to analyze high-quality genome profiling data from diagnostic samples. The generated data will be used to drive the development of statistical models that integrate related and orthogonal datasets (genomic, transcriptome, demographic and clinical variables) to delineate refined ontologies in cancer and deliver comprehensive and personalized prognostication models that take into account the clinical pathophysiological parameters and individual genomic profiles of each sample.
- Studies of clonal heterogeneity. The candidate will apply comprehensive genome profiling approaches to clinical trial cohorts to study molecular and clonal responses to investigational therapeutic interventions. This will form the basis of integrative single-cell genome profiling studies of select patient populations to study profiles of therapeutic resistance and response. This role will apply statistical methodologies to support the generation of high-quality single-cell derived data as well as develop innovative statistical modeling approaches to interrogate the biological and clinical implications of the underlying genomic and clonal variation in cancer.
Responsibilities
- Ensure generation of high-quality genome profiling data and integration of large complex datasets for analysis.
- Develop innovative analytical methods to derive biological and clinical insights from complex genome profiling datasets.
- Develop ideas and drive own independent research, present findings in meetings, and submit research for peer review and publication.
- Work independently and as a team member contributing towards collaborative research initiatives in the laboratory.
Required Qualifications
- PhD in Computational Biology, Biostatistics, Mathematics or a related quantitative field (eg: Statistical Genomics, Physics, Computer Science, Bioinformatics, Statistics).
- Demonstrated experience in applying statistical learning approaches to the analysis of complex and heterogeneous datasets.
- Knowledge of NGS derived data types and related technologies.
- Proficient in at least one of R (preferred), MATLAB.
- Proficient in one or more programming languages (eg: Perl, Python, C)
- Good written and oral English communication skills
- Alternative career paths may be considered for candidates with work experience or equivalent qualifications.
Memorial Sloan Kettering Cancer Center is an equal opportunity employer with a strong commitment to enhancing the diversity of its faculty and staff. Women and applicants from diverse racial, ethnic and cultural backgrounds are encouraged to apply.
To learn more about our lab, please visit us: http://www.mskcc.org/research/lab/elli-papaemmanuil
Please mention [Indeed] in the subject of your emailed application, OR within the body of your email.
Skills :
Areas :
Additional Info:
[Click Here to Access the Original Job Post]