RA in Medical Robotics Available
Position Type: | Administrative |
---|---|
Priority: | No |
Degree Requirement: | MS, PhD |
Research Assistantship in Medical Robotics at Purdue University
School of Industrial Engineering
West Lafayette, Indiana
Requirements
We are looking for an excellent Research Assistant (RA) with a good background in computer vision, robotics and Deep Learning as well as with an interest for medical applications. A high level of programming skills is required (Python, C++, Matlab). The applicant must have completed Master studies in ECE, EE, CS prior to starting the position. The candidate is expected to start in Fall 2018. It is also expected that the candidate will conduct his/her PhD research in this area of work, in the School of Industrial Engineering at Purdue University. A track record including international conferences and journal publications in the area of robotics, deep learning and computer vision is a plus. The PhD student is expected to work with the Da-Vinci and Taurus robots located in campus.
Project Description
This research focuses on the “Medical Robotics Research” topic area. Within this area we will address the following objectives: 1) mitigate the deleterious effects of signal latency on complex telerobotic surgical tasks; 2) extend to full automation when deemed necessary; 3) develop semi-autonomous robotic assistant protocols; and 4) develop models for knowledge representation of semi-autonomous medical behaviors. The specific scope of the PhD student will be aims 3 and 4.
When tele-operation is challenged due to limited bandwidth, latency and lost-of-signal, autonomy needs to step in. While modern robots have been endowed with excellent sensing and dexterous capabilities, there is a gap in knowledge about two critical aspects: (1) how to leverage operator’s expertise under limited connectivity (rather than switching on/off autonomy); (2) how to “transfer” automatically existing abundant knowledge about surgical maneuvers from the operating room (OR) to new, uncontrolled and austere settings. In this research, we will study and theorize about new approaches to predict information to account for randomly delayed and imperfect information due to bandwidth limitations. This will allow us to maximize informational content and minimize redundancy, under constrained communication settings. Based on the predicted information, we will determine at what extent a robot needs to autonomously complete surgical procedures, making use of a sequence of maneuvers
associated with the required surgical procedure extracted from a previously learned library. To populate this library, we will theorize new approaches for transfer learning, where learnt patterns will be projected to fundamentally different domains with variable resource constraints.
In this PhD work, the objective is to develop theoretical framework for supervised autonomy, capable to self-adjust its autonomous behavior and perform procedures in never seen settings using a transfer learning paradigm. Our working hypothesis is that an existing surgical procedure can be adapted to a new domain using an encoding scheme to restore supervisory content combined with a one shot learning framework.
Information and applications
Please send your CV, including your GPA, a list of publications/conferences, and a list of references to:
Applications will be reviewed on a rolling basis, however no later than August 15, 2018.
The successful applicant will have to have ready and available his/her TOEFL, GRE, Transcripts, etc by or before August 15, 2018.