The fellow will focus on the design and development of mechanically intelligent surgical tools that simplify surgical motions and incorporate image sensing. These tools will be integrated with robotic platforms and machine learning algorithms to enable autonomous execution of surgical interventions. The research program will include a systematic investigation comparing this autonomous surgical paradigm against expert surgeons performing equivalent tasks using the current standard of care. The expected output includes peer-reviewed publications on the strategy of blended mechanical and artificial intelligence for autonomous robotic surgery.
In addition to research activities, the fellow will mentor PhD and Master's students in the IMERSE Lab on topics related to autonomous robotics and mechanically intelligent tool design, including strategies for ex vivo and in vivo study design.
Qualifications
- Ph.D. in Mechanical Engineering or a closely related field.
- Experience in surgical robotics, robotic systems design, or medical device development.
- Background in machine learning and/or computer vision as applied to robotics.
- Strong publication record and demonstrated research independence.
The referenced salary range is based on Johns Hopkins University's good faith belief at the time of posting. The actual compensation offered to the selected candidate may vary and will be based on factors including, but not limited to, the experience and qualifications of the selected candidate - e.g., years in rank, training, field, discipline, other work experience, and other similar factors; geographic location; internal equity; external market conditions; and other factors as reasonably determined by the University.
Salary Range
The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.
