Post Doctoral Fellow - PHS/Data Science

Description

POSTDOCTORAL FELLOW IN DATA SCIENCE: OUTBREAK ANALYTICS AND DISEASE MODELING
Title: Postdoctoral Fellow
Location: Department of Public Health Sciences (Clemson University’s main campus, SC)
Term: renewable 12-month position


Clemson University’s Center for Public Health Modeling and Response (PHMR) invites applications for a Postdoctoral Fellowship to join a CDC-funded center grant to create a national network in outbreak analytics and disease modeling. The mission of PHMR is to develop and utilize data-driven approaches to inform clinical and public health decision-making and assist the ability of health organizations and communities to prepare for and respond to, public health threats. The Postdoctoral Fellow will be a part of Clemson’s DMA-PRIME division (Disease Modeling and Analytics to inform outbreak Preparedness, Response, Intervention, Mitigation, and Elimination). For this position, we seek an enthusiastic, collaborative researcher with training in data science with an interest in developing and implementing artificial intelligence (AI) and machine learning (ML) techniques to inform and improve outbreak detection, forecasting, and public health response nationwide.

Duties and responsibilities: The postdoctoral fellowship will focus on developing and implementing AI and ML methodology, with the ultimate goal of detecting/ predicting disease hotspots and informing real-time outbreak response efforts. The models and predictions generated will be integrated into health systems across South Carolina to 1) inform and improve strategic delivery of mobile health clinics for infectious disease testing, treatment, and vaccination, 2) inform community awareness, and 3) run statewide disaster simulation scenarios. The postdoc will be expected to lead publications for the models developed and collaborate on (or lead) publications in public health, medical, and policy journals for research related to the implementation of the methods developed.

Supervision
The Postdoctoral Fellow’s main appointment will be within the Center for Public Health Modeling and Response, located within the Department of Public Health Sciences. Dr. Rennert will serve as the primary advisor, with Dr. McMahan, Dr. Wu, and Dr. Iuricich as co-advisors. The appointment is funded for 2 years, with future years contingent on funding/performance. Salary is highly competitive with a competitive benefits package and negotiable start date.

Qualifications

Required Qualifications
• A PhD in data science or related fields, with demonstrated knowledge in machine learning, deep learning, or statistical analysis.
• Proficiency in programming languages commonly used in data science and AI, with a strong emphasis on Python. Knowledge of other languages such as R, MATLAB, or C plus plus may be beneficial.
• Proficiency in data preprocessing, cleaning, and feature engineering. Familiarity with tools and libraries for data manipulation, such as pandas and NumPy.
• Expertise in machine learning frameworks and libraries, like TensorFlow, PyTorch, or scikit-learn.

Desirable Qualifications
• Specialized expertise in a particular subfield of AI, such as natural language processing, generative adversarial networks, or transformers.
• Domain-specific knowledge, such as healthcare, medicine
• A history of publishing research findings in conferences, journals, or other relevant platforms.

Application Instructions

Application Instructions: Applicants should submit a cover letter, CV, and contact information for three references through Interfolio: http://apply.interfolio.com/136202

Inquiries should be sent to Dr. Lior Rennert (liorr@clemson.edu) and Dr. Federico Iuricich (fiurici@clemson.edu).
Salary: $65,000

Benefits: https://www.clemson.edu/human-resources/benefits/index.html

Equal Employment Opportunity Statement

Clemson University is an AA/EEO employer and does not discriminate against any person or group on the basis of age, color, disability, gender, pregnancy, national origin, race, religion, sexual orientation, veteran status or genetic information.