Publisher
source

Maggie French

5 months ago

Rehabilitation data from routine clinical care University of Utah in United States

Degree Level

PhD, Postdoc

Field of study

Data Science

Funding

The position is fully funded. No further details on stipend amount or tuition coverage are provided.

Deadline

Dec 28, 2026

Country flag

Country

United States

University

University of Utah

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Where to contact

Official Email

Keywords

Data Science
Laboratory Medicine
Health Science
Population Health
Doctoral Studies
Healthcare Delivery
Injury Rehabilitation
Statistic
Postdoc pos

About this position

A fully funded PhD or postdoctoral position is available with Maggie French, Assistant Professor at the University of Utah, USA. The research focuses on generating high quality rehabilitation data from routine clinical care and leveraging this data to improve healthcare delivery. The opportunity is open to candidates interested in clinical science, data science, and rehabilitation research. The successful applicant will join a dynamic academic environment at the University of Utah and work closely with Dr.

French. Funding is fully provided, though specific details on stipend and tuition are not mentioned. The position is set to begin in fall 2026. Interested candidates should have a strong interest in clinical data generation and analysis, and are encouraged to contact Dr.

French directly via email to discuss their background and motivation. The application process is informal at this stage, with direct communication encouraged. This is an excellent opportunity for those seeking to advance their research career in clinical science and data-driven healthcare improvement.

Funding details

The position is fully funded. No further details on stipend amount or tuition coverage are provided.

What's required

Applicants should be interested in generating and analyzing high quality rehabilitation data from routine clinical care. Candidates must be eligible to start a PhD or postdoc in fall 2026. No specific degree, GPA, or language requirements are mentioned, but a background in clinical science, data science, or related fields is likely preferred.

How to apply

Send an email expressing your interest to [email protected]. Mention your background and motivation. Await further instructions from the supervisor.

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