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The Department of Biostatistics at has one postdoctoral fellow position open for biostatistics and data science methodology research in clinical trials. The main focus is research and publication. The primary focus will be to develop novel methods for causal AI/inference methods, adaptive Bayesian clinical trial designs, derive related statistical theory, produce software for implementation, incorporate biomarkers in clinical trial design and analysis, and build statistical learning tools for large data sets. The postdoctoral fellow will work under the supervision of Dr. Liang on challenging and important clinical and biological projects that involve complex statistical modeling, data analysis, and computation.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
Trainee will learn through various research projects, with a primary focus on: (1) developing novel statistical and data science methods, as well as user-friendly software, for integrating AI tools to evaluate novel treatments or design future clinical trials in overall population or subgroups, and (2) analyzing real-world and institutional medical datasets. A major methodological focus will be integrating machine learning/artificial intelligence tools, causal inference methods, Bayesian techniques, and adaptive designs to build innovative, next-generation tools for adaptively and efficiently evaluating treatment effectiveness and learning optimal treatment decisions that may vary by different patients' subgroups.
ELIGIBILITY REQUIREMENTS
Applicants must have a recent PhD in biostatistics or statistics from a reputed University/Institute or within 0-1 years of graduation. At least one first author publication in a peer reviewed journal stemming from PhD studies is required. Candidates must have strong methodological training in statistics or biostatistics, especially in causal inference or semiparametric methods, and have strong computer programming skills, in particular using R or Python. Expertise or skills in the following areas are highly desirable: Causal inference, double/debias machine learning, semiparametric methods, Bayesian MCMC computational methods, adaptive clinical trials, and machine learning for estimation or decision-making.
Please send CV and information on three referees directly to mliang2@mdanderson.org.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html