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Clinical procedures in Operating Rooms (OR) or Image Guided Therapy (IGT) labs are challenging as they are typically multi-disciplinary, dynamic, and often time- and resource-constrained. Their primary features are high complexity, operation in complex time-varying environments, and safety-critical issues. The resulting information overload and associated planning and administrative burden is adversary to the increasing demand for effectiveness and efficiency of an economic and safe patient care workflow. The main goal of this project is to develop solutions for automatic handling of the dynamics of complex clinical procedures, to support efficient usage of operating rooms, interventional labs and available clinical staff. Part of the solution is to optimize the scheduling of clinical procedures by taking in account real-time procedure status updates.
Utilizing real-time data on clinical handling, one may develop model-based or model-free techniques for guiding and predicting workflow processes. To produce a model representing reality precisely, we need a comprehensive grasp of the clinical procedures and a method for generating detailed clinical process models. Recent machine and reinforcement learning advances have delivered promising directions for automatic recognition and prediction of workflow steps. When a massive amount of informative data is provided, advanced ML methods can capture complicated spatio-temporal patterns, as a base for the identification of risks for efficiency and safety. However, due to information limitations, extensive data is seldom accessible in most clinical settings. Within the current project, ample real-world data are available through the use of video-recording of actual procedures and direct read-out of used medical equipment. Existing computer vision and ML methods can be modified to become applicable for autonomous observation, efficiency and safety assessment, by including this readily available information on the procedure steps taken by the clinical staff. Access to this data also allows the rejection of inconsistent models and predictions, and enables the development of more interpretable models and validation in the real-life environment.
The successful candidate will work in the research group Medical Process Engineering and collaborate within the European funded program IWISH with multiple knowledge institutes, clinical partners and medical industry. As a PhD, you will work with at least two academic members of staff and four other PhD candidates that are active in related programs.
BioMechanical Engineering (BMechE) coordinates the Education and Research activities in the field of Mechanical Engineering techniques, like modeling and design, to analyze the interaction between biological and technical systems. The research focuses on four areas: Medical Instruments & Bio-Inspired Technology, Biomechatronics & Human-Machine Control, Biomaterials & Tissue Biomechanics and Delft BioRobotics Lab. BMechE carries out the core of three Master Programmes: MSc BioMedical Engineering (BME), MSc Technical Medicine and MSc Mechanical Engineering (ME). BMechE also contributes to 3 minors: Minor BioMedical Engineering (BME), Minor Geneeskunde/Medicine and Minor Robotica.
We are looking for a talented, motivated and outstanding candidate with an MSc degree (or close to completion) in Technical Medicine, BioMedical Engineering, Physics, Electrical or Mechanical Engineering, Computer Science, Probabilistic Machine Learning, Computer Vision or a related field. The candidate should demonstrate a strong background and/or interest in the fields of Data Science and Machine Learning and affinity with the topic of Medical Workflow Optimization using Artificial Intelligence. The candidate must be enthusiastic and greatly interested in fundamental research in addition to having good programming skills, e.g., in Matlab or Python, for implementing state-of-the-art advanced algorithms. Furthermore, excellent written and oral communication skills in English, affinity with teaching and guiding students and the ability to work in a team and taking initiative are important for this position.
Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.
Fixed-term contract: 4 years.
Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2541 per month in the first year to € 3247 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.
The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.
At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.
Challenge. Change. Impact!
The Faculty of 3mE carries out pioneering research, leading to new fundamental insights and challenging applications in the field of mechanical engineering. From large-scale energy storage, medical instruments, control technology and robotics to smart materials, nanoscale structures and autonomous ships. The foundations and results of this research are reflected in outstanding, contemporary education, inspiring students and PhD candidates to become socially engaged and responsible engineers and scientists. The faculty of 3mE is a dynamic and innovative faculty with an international scope and high-tech lab facilities. Research and education focus on the design, manufacture, application and modification of products, materials, processes and mechanical devices, contributing to the development and growth of a sustainable society, as well as prosperity and welfare.
Click here to go to the website of the Faculty of Mechanical, Maritime and Materials Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.
Delft University of Technology (TU Delft)
Mekelweg 2, 2628 CD, Delft
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