Research Associate or Fellow in Real-time AI for Surgical Robot Control
Surgical & Interventional Engineering
We are seeking a highly motivated individual to join us and work on FAROS, a European research project dedicated to advancing Functionally Accurate RObotic Surgery, https://h2020faros.eu, in collaboration with KU Leuven, Sorbonne University, Balgrist Hospital and SpineGuard.
Functional accuracy is defined as the degree to which the functional outcome of surgery conforms to the expected value for a successful complication-free operation. FAROS aims at improving functional accuracy through embedding physical intelligence in surgical robotics.
Within the FAROS consortium, research in the Department of Surgical & Interventional Engineering within the School of Biomedical Engineering & Imaging Sciences focuses on developing technology for novel image-guided interventions. Deep machine learning is being developed to interpret intraoperative data and link assembled knowledge to autonomously execute surgical actions at operating rates far beyond human response capabilities.
Key activities relate to real-time processing of hyperspectral imaging and its integration in complex robotic systems. The recruited individual will complement our multidisciplinary team and undertake research on machine learning, artificial intelligence and visual servo control of robotically controlled surgical instruments.
The post involves close and active collaboration with researchers, engineers and clinicians. Working with established platforms and building on the software and mechatronics infrastructure already present within our teams if of paramount importance to ensure project cohesion and strong links with the members of the consortium.
The successful candidate will design, develop and translate real-time modular software components for medical data processing, machine learning and visualisation, and also interface those with existing software and hardware components. Specifically, the candidate will develop algorithms that identify and track key anatomical landmarks, and autonomously guide the robot to maximise the informativeness of the captured data (active sensing). The candidate will integrate their developed software with the consortium’s surgical robots.
This post will be offered on an a fixed-term contract for 24 months (latest end date 31/12/2023)
This can be a full-time or part-time post – 50-100% full time equivalent
- Develop, validate and integrate real-time algorithms for computer-assisted intervention (CAI)
- Contribute to project management tasks
- Maintain accurate and up-to date technical and user documentation of the delivered software
- Contribute to the dissemination of the research through publications, open-source software and public engagement activities
The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.
Skills, knowledge, and experience
1. Honours degree (2:1 or above) or equivalent in Mathematics, Engineering, Physics, Computer Science or related numerate discipline
2. PhD or equivalent industrial experience in Computer Assisted Intervention or a closely related field
3. Good knowledge of machine learning and computer vision algorithms
4. Solid knowledge of and experience using the Python programming languages
5. Experience with scientific software packages such as PyTorch, Pandas, SciPy, NumPy, SciKit’s, OpenCV, ROS2, OROCOS, etc.
6. Experience in standard software engineering practices including version control systems and software testing methodologies
7. Experience working on system integration tasks
8. Ability to work with a variety of people
1. A demonstrable record of publications in peer-reviewed conference proceedings and scientific journals
2. Project management experience
3. Understanding of image acquisition and hardware components relevant to real-time data acquisition and processing from existing and medical devices including stereo cameras, force sensors, and robot encoders.
GRADE 7 – as above plus:
1. Active participation in the planning of research projects;
2. The ability to co-ordinate the work of other staff;