human finger pointing inside the head of a robot depicting machine learning.

Personalized Health and Wellness

Faculty Positions

The School of Engineering and Applied Sciences is leveraging an unprecedented faculty hiring opportunity to build on existing strengths at the University at Buffalo in personalized health and wellness.

We intend to hire five faculty across multiple departments to expand a core group that would bring together expertise to enable the creation of whole person digital twins.

This unprecedented faculty hiring initiative is part of the Provost's Advancing Top 25: UB Facuilty Hiring initiative, in the broader  area of  Human Health.

Sensor data input, activity and lifestyle information, genetic and medical information, neural information, etc. all must be collected and processed using AI and Machine Learning (ML) to create the personalized digital twin. The digital twin can then be used to develop, inform, and influence health and wellness for the real person. It can aid in personalized drugs and treatment, and the creation of artificial tissues and organs to replace diseased organs.

This approach requires an interdisciplinary skill set that spans materials, biomedical technology, human factors, and data science, among others.

Departments and desired expertise

This group will benefit from existing expertise across the University at Buffalo including the Jacobs School of Medicine and Biomedical Sciences and the College of Arts and Sciences. Applicants are asked to clearly identify the home department(s) to which their application should be directed. Preferred areas of expertise within the departments are as follows:

Tenure-track Assistant Professor

Expertise focused on neuromodulation, neurostimulation, and biosensors.

Tenured Full Professor

Expertise focused on the creation of digital twins based on genetic, cell, and/or tissue engineering.

Tenure-track Assistant /Associate Professor

Expertise focused on AI, big data, and machine learning for personalized medicine

Tenured Associate Professor

Expertise focused on human factors in personalized healthcare, decision support, and data fusion

Tenured Full Professor

Expertise focused on AI aided molecular discovery for personalized biosensors, implants, and drug delivery systems