The Cazer Lab in the Department of Clinical Sciences, 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 are also developing dashboards and visualization tools for antimicrobial resistance surveillance.
The postdoctoral associate will work primarily on antimicrobial resistance and 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 an unsupervised machine learning methods 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. The postdoctoral associate will play a key role in data management, analysis, and dashboard development for antimicrobial resistance surveillance.
There will be opportunities to develop independently driven projects on infectious diseases and the intersection of epidemiology and machine learning. Qualified candidates will have opportunities to supervise graduate, veterinary, and undergraduate students.
Responsibilities:
• Develop analytic pipelines for multidrug resistance analysis using machine learning techniques
• Examine correlations between genotypic and phenotypic resistance patterns
• Manage multiple datasets for antimicrobial resistance projects
• 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, Epidemiology, 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 SQL, Python, and/or R
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.
If you have any questions regarding this posting, please contact Dr. Casey Cazer (clc248@cornell.edu).
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Pay Ranges:
The hiring rate of pay for the successful candidate will be determined considering the following criteria:
- Prior relevant work or industry experience.
- Education level to the extent education is relevant to the position.
- Academic Discipline (faculty pay ranges reflects 9-month annual salary)
- Unique applicable skills.
finished (2024/01/03, finished 2024/04/01, listed until 2024/07/02)