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"Post-Doctoral Associate - Intelligent Neurotechnology for Translational Medicine"

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Post-Doctoral Associate - Intelligent Neurotechnology for Translational Medicine

Details

Posted: 01-Jul-25

Location: Minneapolis, Minnesota

Categories: Academic/Faculty, Biological/Biomedical Sciences

Internal Number: 368999

The Department of Biomedical Engineering at the University of Minnesota is seeking a highly motivated Postdoctoral Researcher to join a dynamic team focused on developing intelligent neurotechnologies for translational medicine. The lab specializes in computational modeling, physiological signal analysis, and innovative neuromodulation strategies for neurological disorders such as epilepsy, chronic pain, and autonomic dysfunctions.

Primary Responsibilities

  • 35% – Conduct computational modeling and/or analyze data from clinical and preclinical studies related to neurological conditions (e.g., epilepsy, chronic pain, autonomic dysfunctions)
  • 30% – Develop grant-writing skills by preparing postdoctoral fellowship applications and contributing to the development of new research proposals
  • 15% – Present research findings in lab meetings, at national and international conferences, and in peer-reviewed journals
  • 10% – Mentor and collaborate with junior lab members, including graduate and undergraduate students
  • 10% – Assist with lab operations, including maintenance of equipment and coordination of research supplies

Required:

  • Ph.D. in biomedical, electrical, computing, or mechanical engineering, neuroscience, computational biology, or a closely related field
  • Strong oral and written communication skills
  • Demonstrated motivation, initiative, and attention to detail
  • Deep interest in translational neurotechnology, medical device development, and conducting clinical or preclinical proof-of-concept studies

Preferred:

  • Experience in physiological signal processing and the application of machine learning to biomedical data
  • Background in computational modeling for neurobiology, including finite element modeling (FEM) or system identification methods in medical applications

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