Research Associate, Center for Diabetes Technology (UVA)

University of Virginia

3d•Onsite

About The Position

The Center for Diabetes Technology (CDT) and the University of Virginia (UVA) School of Medicine invites applicants for multiple Postdoctoral Research Associates for their research team, focusing on highly complex technology, algorithm development and data management and analysis. CDT is a recognized world leader in the technological treatment of diabetes and the hub of an international research network of leading U.S. institutions. We advance the use of technology, including analytics, to allow improved data and operational management, monitoring, and therapies for patients with Type 1 and Type 2 Diabetes. The Postdoctoral Research Associates will report to Marc Breton, PhD. Dr. Marc Breton was chosen as the recipient of the 2022 Edlich-Henderson Innovator of the Year award. Teaming with Dr. Boris Kovatchev, Breton went on to create the first – and to date only – simulation environment accepted by the U.S. Food & Drug Administration as a replacement for animal studies in pre-clinical assessment of insulin treatment strategies. It paved the way for the pair’s invention of a revolutionary artificial pancreas that could monitor and automatically regulate a person’s blood glucose levels. Overall, Breton has submitted 55 invention disclosures to LVG since 2007, and he is a named inventor on 27 issued U.S. patents. Today, the work that Breton started when he was a post-doc nearly 20 years ago continues to improve the lives of people with diabetes around the world. His work epitomizes UVA’s commitment to biotechnology and its promise of saving and improving lives. https://news.virginia.edu/content/uva-innovator-year-marc-bretons-surprise-journey-diabetes-pioneer The CDT is directed by Boris Kovatchev, PhD, a world-renowned researcher in the field of Diabetes, algorithm development and data science. Dr. Kovatchev is a regular lecturer at Universities and Conferences throughout the US and Europe. He is actively involved with Center projects and with the direction and education of all post-doctoral candidates. With substantial NIH funding, the Center led and successfully completed the three largest randomized Artificial Pancreas(AP) clinical trials in the World to date, including a pivotal trial aiming for regulatory clearance of a new AP System (that included the algorithm program developed at the Center), published on October 16, 2019 in the New England Journal of Medicine. For more information visit: https://med.virginia.edu/diabetes-technology/ Successful candidates will be afforded the opportunity to continue their research under the supervision of a team of accomplished faculty members and physicians. As well, incumbents will be able to conduct independent translational research in biotechnology, biomedical engineering, advanced mathematics, and Data Science that will not only be essential to the programs of CDT faculty members; but will impact future healthcare programs and therapeutics for improved patient care. Incumbents will perform high-profile, NIH-funded and other sponsored research in an interdisciplinary international research setting and be a regular attendee at diabetes and technology conferences. In addition to the above job responsibilities, other duties may be assigned. There are no clinical responsibilities associated with this position. However, the successful candidate will have regular access to physicians through the UVA School of Medicine.

Requirements

Doctoral degree completed at time of hire.

Nice To Haves

Research experience in machine learning, biotechnology, biomedical engineering, advanced mathematics or (Diabetes) Data Science.

Responsibilities

Conduct supervised and independent research for the Center for Diabetes Technology and expand Center-related research lines in:
Use of machine learning methods for risk prediction, control, classification, and treatment progression
Metabolic and behavioral computer simulations related to diabetes and pre-diabetes.
Modeling, simulation and model-based analysis of glucose metabolism and control.
Validation and testing of in silico-designed treatments and diabetes-related technology.