Postdoctoral Fellow - Autonomous Surgical Robotics

VS-Postdoc; Baltimore, Northern (MD, KY); Hybrid; USD 65,000 - 90,000; Full time

Job summary

Johns Hopkins University’s IMERSE Lab in the Department of Mechanical Engineering invites applications for a Postdoctoral Fellow in autonomous surgical robotics. The fellow will lead research and development of novel tools and robotic platforms for autonomous interventions.

Responsibilities include mentoring PhD and Master’s students, integrating image sensing with mechanical design, and conducting rigorous comparisons against current standards, with publications and impact.

Qualifications

• Ph.D. in Mechanical Engineering or a closely related field.
• Experience in surgical robotics or medical device development.
• Background in machine learning and computer vision applied to robotics.
• Strong publication record and demonstrated research independence.

Responsibilities

• Lead research and development of autonomous surgical robotics tools and systems.
• Mentor PhD and Master’s students in IMERSE Lab topics related to autonomous robotics.
• Integrate sensing, mechanical design, and autonomous control; compare with current standards.
• Publish findings and contribute to advancing autonomous robotic surgery.

Skills

Surgical robotics

Machine learning

Computer vision

Research independence

Education

PhD in Mechanical Engineering

Tools

Python

MATLAB

ROS

Job description

Postdoctoral Fellow - Autonomous Surgical Robotics

2026-08-10

Postdoctoral Fellow - Autonomous Surgical Robotics

Johns Hopkins University

Type: Full-Time

Posted: Today

Category: Mechanical Engineering

The IMERSE Lab in the Department of Mechanical Engineering at Johns HopkinsUniversity invites applications for a Postdoctoral Fellow position in autonomoussurgical robotics. The successful candidate will lead the research anddevelopment of novel surgical tools and robotic systems for autonomous surgicalinterventions.

The fellow will focus on the design and development of mechanicallyintelligent surgical tools that simplify surgical motions and incorporate imagesensing. These tools will be integrated with robotic platforms and machinelearning algorithms to enable autonomous execution of surgical interventions.The research program will include a systematic investigation comparing thisautonomous surgical paradigm against expert surgeons performing equivalent tasksusing the current standard of care. The expected output includes peer-reviewedpublications on the strategy of blended mechanical and artificial intelligencefor autonomous robotic surgery.

In addition to research activities, the fellow will mentor PhD and Master'sstudents in the IMERSE Lab on topics related to autonomous robotics andmechanically intelligent tool design, including strategies for ex vivo and invivo study design.

Qualifications

Ph.D. in Mechanical Engineering or a closely related field.

Experience in surgical robotics, robotic systems design, or medical devicedevelopment.

Background in machine learning and/or computer vision as applied torobotics.

Strong publication record and demonstrated research independence.

The referenced salary range is based on Johns Hopkins University's good faithbelief at the time of posting. The actual compensation offered to the selectedcandidate may vary and will be based on factors including, but not limited to,the experience and qualifications of the selected candidate - e.g., years inrank, training, field, discipline, other work experience, and other similarfactors; geographic location; internal equity; external market conditions; andother factors as reasonably determined by the University.

Salary Range

The referenced salary range represents the minimum and maximum salaries forthis position and is based on Johns Hopkins University's good faith belief atthe time of posting. Not all candidates will be eligible for the upper end ofthe salary range. The actual compensation offered to the selected candidate mayvary and will ultimately depend on multiple factors, which may include thesuccessful candidate's geographic location, skills, work experience, internalequity, market conditions, education/training and other factors, as reasonablydetermined by the University.

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