Postdoctoral Research Associate in Statistical Methods for Cancer Research with Electronic Health Records

The Department of Biostatistics at Brown University is recruiting a postdoctoral research associate with interest in conducting research to advance the study of cancer screening, treatment and survivorship using electronic health records. Relevant expertise includes methods for causal inference, missing data, informative observation processes, outcome misclassification, and algorithmic fairness. The two-year position will involve conducting research on development and application of statistical methodology to healthcare-derived data including electronic health records (EHR) and medical claims data.

Current research projects include developing methods to address algorithmic fairness in studies using EHR data, account for informative observation processes, combine EHR and clinical trial data, and account for missing and irregularly collected confounder data in target trial emulation. The postdoctoral associate will be primarily mentored by Prof. Rebecca Hubbard and will have opportunities to develop and apply novel methods to address real-world questions in comparative effectiveness of cancer treatments and cancer screening regimens.

The successful candidate will have interest and expertise in addressing complex, real-world problems using modern methods in statistics, data science and computing. The position provides an outstanding opportunity for those seeking to develop and apply statistical methods that leverage complex, modern data sources to improve health and healthcare.

Qualifications

The successful candidate will have interest and expertise in addressing complex, real-world problems using modern methods in statistics, data science and computing. The position provides an outstanding opportunity for those seeking to develop and apply statistical methods that leverage complex, modern data sources to improve health and healthcare.

Qualifications include a PhD degree or equivalent in biostatistics, statistics, machine learning, computer science, or related quantitative field with considerable knowledge and experience in statistical computing. Candidates with experience and interest in methods for causal inference, methods for electronic health record data, and interest in conducting research at the intersection of data science and clinical epidemiology are especially encouraged to apply. Excellent oral and written and communication skills are required.

The Department of Biostatistics believes that diversity, equity, and inclusion are essential to achieving our mission of advancing scientific knowledge and improving human health. We are committed to creating a welcoming and supportive environment that values and respects the diverse backgrounds, experiences, and perspectives of all members of our community. We recognize that diversity encompasses a wide range of characteristics and believe that a diverse and inclusive community fosters creativity, innovation, and excellence in research. The department is dedicated to recruiting and retaining talented individuals from underrepresented groups.

Application Instructions

Candidates should apply with a cover letter (summarizing research experience, qualifications, career goals, as well as addressing the reasons for their interest in the position and their commitment to diversity, equity and inclusion, and/or how they intend to show this commitment in future work through scholarship, teaching, mentoring, service and community engagement), curriculum vitae, sample publication or equivalent, and the names and contact information for 3 references. We value the different ways that a candidate's commitment to diversity, equity and inclusion might be demonstrated including in scholarship, teaching, mentoring, service and community engagement.

Candidates should apply online in Interfolio. Please indicate in your cover letter the search number (PH 337).


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