PhD Scholarship Position in Machine Learning for Surgical Performance Assessment

Applications are invited for a PhD position in applied mathematics to join ETH Zurich, Switzerland. Applications should be submitted as soon as possible.

ETH Zurich is one of the world's leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. new position.The position will be part of a collaboration with a manufacturer of surgical training simulators. The overall aim of the project is to develop and use machine learning techniques for providing these simulators with automatic performance assessment and feedback functions in order to enhance their effectiveness as educational instruments.

Being able to give automatic performance assessment and feedback depend on the availability of adequate machine-level representations of expert knowledge. Our general approach is to model surgical procedures as sequential decision making tasks, and to learn such representations of expert knowledge from the behavioral characteristics of data generated by simulator users. One aspect we are particularly interested is to develop automatic teaching methods that can adaptively provide trainees with demonstrations of optimal behavior.

Within the project, there will be opportunities to work in the following directions: Surgical Performance Assessment, Surgical Activity Recognition, Inverse Reinforcement Learning, Machine Teaching, Representation Learning, Generative Modelling.

Applicantsshould have completed, or be about to complete, a Master’s degree in computer science, electrical engineering, applied mathematics or a similar technical field. Applicants should have a solid knowledge of machine learning as well as strong mathematical and programming skills. Moreover, applicants should be able to work independently and be interested in engaging actively with an industry partner.

Deadline: As soon as possible
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