Jobs

Endometriosis is one of the most common gynecological conditions affecting 10-15% of women of reproductive age, leading to chronic pain and reduced fertility. In Bavaria alone, approximately 50,000 cases are reported annually. In a multidisciplinary team consisting of 7 different labs at 4 Universities in Bavaria, our innovative research focuses on integrating multimodal functional and structural non invasive imaging methods with artificial intelligence to create a comprehensive 3D pelvic model and provide early detection of endometriosis as well as surgical support. The model is further enriched with intraoperative and postoperative data to improve diagnostic and surgical outcomes.
The focus of this doctoral position is the development and implementation of an AI-driven intraoperative assistance system. This system is designed to enhance the planning and execution of laparoscopic procedures for the diagnosis and surgical treatment of endometriosis. The system aims to assist surgeons by improving visualization, navigation, and decision-making during surgery.

Key Responsibilities:

•  AI-Driven Detection and Segmentation:
  • Develop methods for semantic segmentation and detection of endometriosis lesions from endoscopic video streams.
  • Implement AI tools for intraoperative labeling and dynamic updates of the deformable patient-specific pelvic model.
  • Automate classification of anatomical structures and potential endometriosis lesions during surgery.
• Preoperative Support and Planning:
  • Design risk-aware opera on planning features using an AI-enhanced 3D pelvic model.
  • Integrate modules that analyze historical patient data and anatomical structures to propose personalized surgical plans and steps.
  • Optimize instrument paths and es mate the number, size, and grade of endometriosis lesions to prepare the surgical team effectively.
• Human-Machine Interface Design:
  • Develop an intuitive human-machine interface to guide surgeons during the operation, including augmented reality overlays and real-time annotations.
  • Implement optional voice-controlled features for interactive feedback and real-time updates on lesion status during surgery.
• Validation and Deployment:
  • Develop and refine AI methods on retrospective datasets and validate their effectiveness independently on prospective data in a clinical study.
  • Collaborate with clinical teams to ensure the system aligns with surgical workflows and enhances operational efficiency.

We invite applications for a doctoral research position as part of the TED-MeD project, an initiative aimed at the development and approval processes for medical technology products. The TED-MeD project focuses on creating automated safety and compliance testing methodologies, enabling faster and more reliable regulatory approval for complex medical devices including AI components. Central to the project is the development of an AI-supported assistance system, which will guide developers in identifying safety and regulatory risks early in the design process and evaluating the impact of design changes on compliance. As a concrete application within TED-MeD, this project focuses on the development of a robotic catheter manipulator.

The focus of this doctoral position is the development and implementation of the catheter navigation robot for endovascular interventions. This position involves addressing challenges at the intersection of medical robotics, regulatory requirements, and user-centric design. The candidate will play a crucial role in the design, implementation, and evaluation of the robotic system, advancing precision and safety in endovascular procedures.

As part of this role, the researcher will conduct an in-depth analysis of application scenarios to determine the specific requirements for the system, including technical and regulatory aspects. Based on these requirements, the candidate will design and construct a catheter manipulator that prioritizes usability, modularity, flexibility, safety, and sterilizability. The development process will include creating operational and path-planning strategies to enable precise catheter navigation through vascular structures to target locations.

A key focus of the project will be the development and testing of control strategies, including telemanipulation, shared control, and autonomous navigation modes, based on previous work at SPARC. The candidate will also work on designing intuitive user interfaces to ensure seamless interaction between the robotic system and its users. Ultimately, the developed components will be integrated into a complete robotic system and rigorously evaluated to ensure compliance with safety standards and optimal performance.