The Center for Targeted Machine Learning and Causal Inference (CTML) in Berkeley Public Health’s Divisions of Epidemiology and Biostatistics is an interdisciplinary hub that brings together data scientists, statisticians, computer scientists, and health professionals. Its mission is to improve the research and practice of health sciences by developing and applying targeted learning analytics to solve urgent health problems.
CTML is seeking a highly motivated and talented Postdoctoral Scholar to join our interdisciplinary research projects focused on causal inference, epidemiology, and pharmaceutical research. The successful candidate will work on and manage innovative projects aimed at understanding the causal relationships between pharmaceutical interventions and health outcomes. This position offers a unique opportunity to coordinate several extensive biostatistical research collaborations and contribute to cutting-edge research at the intersection of public health, epidemiology, and biostatistics, with an emphasis on applying advanced causal inference methods to real-world data.
Key Responsibilities:
- Research Design & Analysis: Lead the design, execution, and analysis of CTML’s Industry-partnered studies utilizing causal inference methods.
- Methodological Development: Develop and apply novel causal inference methodologies to address biases in observational studies, such as confounding, selection bias, and measurement error.
- Data Management: Work with large-scale healthcare databases, electronic health records (EHRs), and other real-world data sources to perform rigorous epidemiological analyses.
- Manage Collaborations & Communication: Strategically steer multidisciplinary teams, including epidemiologists, biostatisticians, clinicians, and data scientists, to interpret findings and drive them into actionable insights.
- Manuscript Preparation: Prepare manuscripts for peer-reviewed publication and present findings at scientific conferences.
- Grant Writing: Assist in preparing grant proposals to secure funding for future research projects.
Beyond the responsibilities of coordinating existing projects and managing the development of scientific papers, multiple professional development opportunities will be offered, including developing new working groups and projects and first-authoring academic publications.
Center: https://ctml.berkeley.edu/
Qualifications
- Doctoral degree (or equivalent international degree), or enrolled in a Doctoral or equivalent international degree program at the time of application.
- Doctoral degree (or equivalent international degree). No more than three years of postdoctoral experience at the time of appointment.
- Doctorate in Biostatistics, Statistics, Epidemiology, machine learning, or related field.
- Experience in statistical computing skills, especially in R
- Experience/working skills in data preprocessing/cleaning, statistical analysis, systems programming, database design and data security measures, especially regarding large datasets.
- Experience in data analysis consultation.
- Experience in software package development and maintenance.
- Knowledge of causal inference and machine learning methodologies, design of clinical trials, and analysis of electronic health record data.
Application Requirements
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Curriculum Vitae – Your most recently updated C.V. -
Cover Letter – 1-2 Pages -
Statement of Research (Optional) -
Statement on Contributions to Advancing Diversity, Equity, and Inclusion – Statement on your contributions to diversity, equity, and inclusion, including information about your understanding of these topics, your record of activities to date, and your specific plans and goals for advancing equity and inclusion if hired at Berkeley. More Information and guidelines.
- 3 required (contact information only)
