In recent years we have mapped out the genetic alterations that define each cancer type. The next challenge is to bring these findings forward to support clinical decisions and improve outcomes for cancer patients.

Our laboratory performs population genomic approaches in thousands of cancer patients (>10,000) with the view to characterize disease defining, prognostic and treatment predictive biomarkers. Our vision is to develop a suite of clinical decision support tools informed by large knowledge banks of cancer patients and deliver patient specific genetic annotations, treatment decision support and outcome predictions and implement them as applications in web portal format and make them available to the international clinical and research community.

Beyond model development, we are investing heavily in the characterization of integrative single cell RNA profiling approaches to define the downstream effectors of disease biology. Matched to clinical trial samples obtained before, during and after therapy we aim to characterize profiles of response and resistance to targeted agents and develop rationalized approaches to combination therapies.

Our lab is a collective of computational, clinical and molecular biologists. Our emphasis is in developing an open access, knowledge sharing environment and work tirelessly to bridge the knowledge gaps required to develop molecularly informed medicine approaches.

We are looking for a motivated driven individual with experience in statistical learning and advanced modeling methodologies to join our team. The applicant would work closely with research and clinical scientists to analyze large scale genome data generated in our group with the purpose to:

• Test and develop robust analytical methodologies for data evaluation (derived from sparse and noisy population level or single cell derived genomic analysis).
• Develop unsupervised classification methods using molecular and clinical variables.
• Study genotype correlations to diagnostic variables, disease morphology and outcome endpoints.
• Develop integrative prognostic and predictive models that consider demographic, genomic clinical and intermediate therapeutic intervention steps.

The applicant should be independent in using and applying a diverse array of analytic techniques routinely used in the health sciences including sampling, logistic regression, multiple regression, multi-level modeling, weighting techniques, sampling tests and power calculations.

Qualifications
Minimum Requirements:

• Degree in Statistics, Mathematics, Engineering, Physics or related quantitative discipline.
• At least 2 years experience, or applicant must represent top 5% in class with references in strong support of minimum requirements listed below.
• Independent on quantitative analysis and data interpretation.
• Experience with advanced statistical analysis, mathematical modeling and programming tools.
• Excellent organizational skills, commitment to generating accurate data, ability to meet deadlines, and demonstrated experience in multi-tasking.
• Strong interpersonal and teamwork skills, and aptitude in interacting with clinical and research collaborators.
• Strong oral and written communication skills.
• Candidates with these desired skills will be given preferential consideration:

Alternative career paths may be considered and we are open to role definition for candidates with work experience or equivalent qualifications.

Additional Information
For more information about our laboratory or to apply for one of the postdoctoral positions within our lab, please visit the links below.

Please do not hesitate to contact us should you have any questions about our laboratory, and to address any informal questions or inquiries to Elli Papaemmanuil at [email protected].
 

Laboratory Website

https://www.mskcc.org/research-areas/labs/elli-papaemmanuil