Postdoctoral Research Associate in Data-Driven AI and ML-AI.Health4All- College of Medicine

About the University of Illinois ChicagoUIC is among the nation’s preeminent urban public research universities, a Carnegie RU/VH research institution, and the largest university in Chicago. UIC serves over 34,000 students, comprising one of the most diverse student bodies in the nation and is designated as a Minority Serving Institution (MSI), an Asian American and Native American Pacific Islander Serving Institution (AANAPSI) and a Hispanic Serving Institution (HSI). Through its 16 colleges, UIC produces nationally and internationally recognized multidisciplinary academic programs in concert with civic, corporate and community partners worldwide, including a full complement of health sciences colleges. By emphasizing cutting-edge and transformational research along with a commitment to the success of all students, UIC embodies the dynamic, vibrant and engaged urban university. Recent “Best Colleges” rankings published by U.S. News & World Report, found UIC climbed up in its rankings among top public schools in the nation and among all national universities. UIC has over 300,000 alumni, and is one of the largest employers in the city of Chicago.Benefits eligible positions include a comprehensive benefits package which offers: Health, Dental, Vision, Life, Disability & AD&D insurance; a defined benefit pension plan; paid leaves such as Vacation, Holiday and Sick; tuition waivers for employees and dependents. Click for a complete list of Employee Benefits. Position SummaryThe AI.Health4All Center for Health Equity using Machine Learning and Artificial Intelligence at the University of Illinois-Chicago (UIC) College of Medicine is seeking applications for a highly motivated and skilled Postdoctoral Researcher to work on data-driven patient flow optimization aimed at reducing hospital crowding and improving health for all using integrated methodologies.In healthcare systems, patient flow refers to the movement of patients through various stages of care processes, from admission to discharge. Efficient patient flow management is a critical determinant of healthcare quality and operational efficiency. Poorly managed patient flow can lead to delays, overcrowding, resource utilization inefficiencies, and compromised patient outcomes. Despite significant advancements in healthcare technology, many systems continue to face challenges related to bottlenecks, unpredictable delays, and suboptimal resource allocation, leading to hospital crowding, a worldwide public health problem.

Traditional methods for improving patient flow often rely on static processes and subjective decision-making, which are insufficient for addressing the complex, dynamic nature of modern healthcare environments. Recent developments in data-driven approaches, such as artificial intelligence (AI), process mining, complex network analysis, and simulation, offer powerful tools for optimizing patient flow. This research project aims to implement integrated and data-driven methodologies (artificial intelligence (AI), process mining, complex network analysis, and simulation) to create a comprehensive framework for optimizing non-trauma emergency patient flow and reducing hospital crowding and improving health for all. The successful candidate will analyze large-scale patient flow data, develop and implement computational models, and design responsible AI-driven solutions for emergency care systems.

Duties & ResponsibilitiesResearch Design: Conduct research on patient flow optimization using data-driven and integrated approaches, including process mining, social network analysis, machine learning, and simulation techniques.Data Collection and Analysis: Conduct surveys, interviews, and observations to establish research protocols and identify gaps.Algorithm Development: Develop explainable, responsible, and generalizable ML models for triage and resource allocation and evaluate their impact using appropriate key performance indicators (KPIs).