Postdoctoral Scholar - Center for Targeted Machine Learning and Causal Inference (CTML) - School of Public Health

Berkeley Public Health (BPH) aims to improve population health, especially for the most vulnerable, through interdisciplinary collaborations, preeminent education, and transformational research. Established in 1943, BPH is a professional school on the UC Berkeley campus that comprises six academic divisions and nearly 30 research centers and programs. Our School values include social justice, health as a right, challenging conventional thought, embracing diversity, and creating meaningful impact. We honor our principles of community by centering and valuing everyone in our community, prioritizing prevention while remaining grounded in social justice, promoting safety and respect, practicing self-care and kindness, and remaining optimistic, hopeful, and committed to change. Learn more athttps://publichealth.berkeley.edu.

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 projectsandmanaging 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/

• 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.

• 3 required (contact information only)

UC Berkeley is committed to diversity, equity, inclusion, and belonging. The excellence of the institution requires an environment in which the diverse community of faculty, students, and staff are welcome and included. Successful candidates will demonstrate knowledge and skill related to ensuring equity and inclusion in the activities of their academic position (e.g., teaching, research, and service, as applicable).

The University of California, Berkeley is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

Please refer to the University of California's Affirmative Action Policy and the University of California's Anti-Discrimination Policy.

In searches when letters of reference are required all letters will be treated as confidential per University of California policy and California state law. Please refer potential referees, including when letters are provided via a third party (i.e., dossier service or career center), to the UC Berkeley statement of confidentiality prior to submitting their letter.

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.