Postdoctoral Associate in Statistical Modeling of Cancer Screening and Surveillance Data

The International Society for Bayesian Analysis; Durham (NC); On-site; USD 60,000 - 80,000; Full time

Job summary

Duke University seeks a Postdoctoral Associate to analyze longitudinal cancer screening data at the Ryser Laboratory. This 2-year position (with potential renewal) involves modeling and statistical analysis in collaboration with clinical teams, requiring strong expertise in (bio)statistics and relevant programming skills.

Qualifications

• Proficiency in stochastic processes.
• Extensive experience with real-world cancer data.
• Track record of first-author publication in peer-reviewed journals.

Responsibilities

• Analyze and model longitudinal datasets from cancer patients.
• Development of applied methods for statistical analysis.
• Collaborate with clinical partners on modeling projects.

Skills

Longitudinal data analysis

Joint modeling

Bayesian inference

Statistical programming

Education

Doctorate in (bio)statistics, applied mathematics, epidemiology, or a related discipline

Job description

Postdoctoral Associate in Statistical Modeling of Cancer Screening and Surveillance Data

The Ryser Laboratory at Duke University (Durham, NC, USA) is looking for a highly motivated Postdoctoral Associate in the fields of longitudinal data analysis and joint modeling of cancer screening and surveillance data. The position will be funded by Marc D. Ryser, PhD who holds a joint appointment between the Departments Population Health Sciences and Mathematics. The appointment is for 2 years, starting March 1, 2022, or later, and may be renewed for 1 year.

OVERVIEW. The successful applicant will use tools from statistics, biostatistics and mathematics to analyze and model rich longitudinal datasets from breast and brain cancer patients. Possible projects include (i) model-based inference of breast cancer overdiagnosis from a large longitudinal mammography registry; (ii) joint modeling of surveillance patterns and disease recurrence in a large, newly established cohort of patients diagnosed with early-stage breast cancer; and (iii) joint modeling of surveillance imaging (MRI) and disease progression in low-grade glioma patients. All three projects will require a certain degree of applied methods development. The work will be performed in a cross-disciplinary setting, together with clinical collaborators.

QUALIFICATIONS. The successful applicant has a doctorate in (bio)statistics, applied mathematics, epidemiology, or a related discipline. The position requires proficiency in stochastic processes, longitudinal data analysis, joint modeling of longitudinal processes, and Bayesian inference techniques. Extensive experience with real-world (cancer) data and advanced statistical programming skills are required. The successful applicant demonstrates great attention to detail, works independently, takes initiative, has excellent written and verbal communication skills, and performs well in an interdisciplinary environment at the interface between the quantitative sciences and medicine. The successful candidate is expected to have a track record of first-author publication in peer-reviewed journals.

APPLICATION. Interested applicants should submit the following documents:
• Cover letter describing research interests and career goals (2 pages or less)
• Curriculum vitae (4 pages or less)
• Copies of 1-2 relevant first-author publications
• A list containing the contact information for 3 references

Completed applications will be reviewed on an ongoing basis until the position is filled, with priority given to applications submitted prior to January 5, 2022. Questions may be directed to [e-mail].

Duke University is an Affirmative Action/Equal Opportunity Employer committed to providing employment opportunity without regard to an individual’s age, color, disability, genetic information, gender, gender identity, national origin, race, religion, sexual orientation, or veteran status.