The Toosizadeh Lab in the Department of Biomedical Engineering and Medicine at the University of Arizona is seeking a highly motivated postdoctoral candidate to work on translational projects at the intersection of biomechanics, frailty and cognitive impairment assessment, and machine learning. Within this research program we aim to address to critical aging research questions: 1.) Can a dynamic stress-response model of physiological systems using a sub-maximal stress test replicate the results of real-life stress (such as invasive therapies)? 2.) Can and to what extent considering multiple physiological systems and interactions between them explain frailty characteristics in the stress-response model?

An important component of this position will be to work as part of an interdisciplinary team spanning across colleges of engineering, medicine, and science. The successful candidate will be required to lead independent research projects, work closely with medical collaborators, mentor students, and publish high-quality research each year. This is a year-to-year appointment with the possibility of renewal for up to three years.

Outstanding UA benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; access to UA recreation and cultural activities; and more!

The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocations services, please click here .

• Conduct research in the area of aging, frailty, and biomechanics.
• Data Post-processing.
• Programming (using MATLAB or Python)
• Publish journal articles.
• Presentation of research at national and international conferences.
• Supervise undergraduate and graduate students. 

Knowledge, Skills, and Abilities:

• Knowledge of MATLAB, Python, or similar programming skills.
• Excellent written and oral English language skills.

• PhD in computer science, biomedical engineering, mechanical engineering, or a closely related discipline. 
• The selected candidate must have a conferred PhD upon hire. 

• Experience in Machine Learning, human performance analysis, wearable sensors, fMRI, fNIRS, and/or ECG.
• Evidence of strong publication record.