Career Profiles

Focusing on the real-world requirements in AI in Pharmaceutical Outcomes and Policy Research, this focus area provides students with the knowledge and skill set in machine learning, data mining, measurement, causal inference, prediction models, bias mitigation, and decision support systems. Students work with excellent researchers with expertise in pharmaceutical outcomes and artificial intelligence research, and the skills they acquire are highly valued in academia and industry.

Having gained unique and highly-demanded skills, trainees of AI in Pharmaceutical Outcomes and Policy Research are well-positioned to join various academic and research institutions worldwide. In addition, the Health Economics and Outcomes Research (HEOR) and Real-World Evidence (RWE) departments in various industrial and government entities will have a keen interest in our graduates. In addition, our graduates will be highly sought after by various Health Economics and Outcomes Research (HEOR) and Real-World Evidence (RWE) departments within industries and government.

For instance, at the FDA, our students could be of interest to the Center for Drug Evaluation and Research (CDER)-Office of Surveillance and Epidemiology. With AI/ML and NLP competencies (e.g., training and hands-on experience in data manipulation, feature selections, and model development combined with knowledge in designing epidemiological studies), our future graduates from this track could also potentially fit into the CDER-Office of Translational Research (OTS).

In addition, CDC recruits postdocs annually through the Prevention Effectiveness (PE) Fellowship program in different centers (e.g., Injury Center, National Center for Chronic Disease Prevention and Health Promotion – Division of Diabetes Translation, etc.), with a focus on AI/ML applications in their projects.

The pharmaceutical industry will also be highly interested in graduates of our AI focus area. For instance, Johnson & Johnson and Merck are particularly interested in Artificial Intelligence and Machine Learning, and Moderna has just launched an AI Academy. It is part of Novartis’s strategy to develop, experiment, and scale AI-based solutions, and Pfizer uses AI for a variety of purposes, such as predicting drug efficacy and side effects and managing the enormous amounts of documents and data that support pharmaceutical products.

There will also be a high demand for our trainees among large tech companies. IBM Watson has a specialized Health department focusing on clinical information extraction and AI-based decision support. Microsoft Cloud for Healthcare provides AI solutions to improve health data insights by connecting data and using predictive analytics to identify clinical trends. Google Health develops various AI-based solutions and tools for consumers, communities, and researchers; and NVIDIA builds new AI hardware and software for high-performance computing (HPC) to support healthcare research.

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