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Postdoctoral Associate Position in Computational Approaches to Antimicrobial Resistance Surveillance
Dr. Casey Cazer
Department of Public and Ecosystem Health
College of Veterinary Medicine, Cornell University
Ithaca, NY

The Cazer Lab in the Department of Public and Ecosystem Health, Cornell University College of Veterinary Medicine is seeking an outstanding postdoctoral associate in the area of antimicrobial resistance surveillance, using methods from computational biology, machine learning, and statistics.

The Cazer Lab broadly investigates the epidemiology of zoonotic and infectious diseases with computational and field studies. We are primarily interested in the development and spread of antimicrobial resistant bacteria among animals and humans. Specifically, we are investigating novel computational approaches to antimicrobial resistance surveillance to detect increases in resistance and quantify the impacts of mitigation approaches, such as antimicrobial use restrictions in livestock. We also have projects investigating syndromic surveillance for COVID-19 in institutional and community settings, as well as the prevalence and persistence of immunity post-vaccination and post-natural infection.

The postdoctoral associate will work primarily on multidrug resistance surveillance. Multidrug resistance complicates treatment of bacterial infections, therefore early knowledge of circulating resistance phenotypes can improve patient level outcomes and judicious antimicrobial use. However, the number of potential multidrug resistance patterns is exponentially large. We use association mining, an unsupervised machine learning method, to identify and quantify multidrug resistance patterns in phenotypic antimicrobial resistance data. We plan to incorporate genomic sequencing data into this surveillance approach and are interested in exploring new machine-learning techniques for multidrug resistance surveillance in human and animal populations.

There will be opportunities to develop independently driven projects on infectious diseases and the intersection of epidemiology and machine learning. We have access to laboratory diagnostic results, veterinary and human medical records, and national antimicrobial resistance surveillance data. Qualified candidates will have opportunities to supervise graduate, veterinary, and undergraduate students.

Responsibilities:

• Develop analytic pipelines for multidrug resistance analysis using association mining and/or other unsupervised machine learning techniques
• Examine correlations between genotypic and phenotypic resistance patterns
• Collaborate with subject matter experts in antimicrobial resistance and microbiology
• Draft manuscripts and publish results in high-quality, peer-reviewed journals
• Use best-practices for reproducible coding
• Present results at conferences and seminars
• Contribute to developing and writing grant proposals

Qualifications:

• Ph.D. degree in Biostatistics, Bioinformatics, Applied Statistics, Computer Science, Mathematics, or a related discipline or a health professional doctorate (e.g., DVM, MD) with substantial experience in one of these fields.
• Experience working with biomedical data preferred.
• Knowledge of epidemiologic principles preferred.
• Must be able to work independently and as part of a diverse, interdisciplinary team.
• Strong analytic and written communication skills are required.
• Proficiency in R (preferred) or other data analysis software

This is a 1-year, full-time appointment with a potential for extension contingent on funding and successful performance. This position will start immediately, and work may be conducted partially or fully remotely.