This position is based in the Perkó lab at the Department of Radiation Science and Technology (AS Faculty), under joint supervision of Zoltán Perkó and Elizabeth Carroll (Department of Imaging Physics, AS faculty). You will focus on developing and applying state-of-the-art generative, reinforcement learning and transformer methods to improve numerous aspects of the radiotherapy (RT) treatment planning processes.

Radiotherapy is a key treatment modality for cancer, but side-effects can have significant negative impact on patients' quality-of-life. Intensity-Modulated Proton Therapy (IMPT) is one of the newest type of RT, holding the promise of better preserving normal tissues surrounding the tumor, due to the finite range of protons. However, IMPT's advantage also comes at the cost of increased sensitivity to uncertainties in patient alignment and dose calculation, or changes in patient anatomy, which can never be completely eliminated. To maximize the effectiveness of RT, treatment sensitivity should be minimized and irradiations should optimally be adapted to the changing patient anatomy, in order to utilize the superior normal tissue sparing potential of IMPT without degradation.

To achieve this, first you will develop generative methods based on population information to capture anatomical variations and generate virtual patient specific anatomies that can be used in robust and probabilistic treatment planning processes. Second, you will develop methods to build a patient-specific virtual RT environment predicting the impact of radiation treatments under varying beam arrangements and fractionation schedules, and will study reinforcement learning methods interacting with this virtual RT environment to derive optimal treatment plans for the changing patient anatomy. Last, the developed generative and reinforcement learning models will be coupled to transformer based dose calculation algorithms to enable real-time irradiation adaptation. Depending on time and interest, you may also study extensions of the generative algorithms to deal with missing/low quality imaging information (e.g., from cone-beam CT), crucial in the RT process and also in certain biomedical imaging tasks.

This one of four PhD postions within the BIOLAB, for a complete overview check: https://www.tudelft.nl/ai/biolab

In the Biomedical Intervention Optimization lab (BIOlab), experts in computer vision (Tömen), reinforcement learning (Böhmer), neural architecture (Brinks), deep learning and computational physics (Perkó), and biomedical imaging (Gruβmayer, Carroll) join forces to create high-efficiency, real-time, AI-driven feedback and control in biomedical applications.

To qualify for this position you must have:

• An MSc degree (or equivalent) in applied physics, applied mathematics, medical physics, biomedical engineering, computer science or a strongly related field.
• Solid background in applied mathematics and computational physics, especially in probability theory, numerical analysis, statistics and algorithm development.
• Demonstrated experience in programming in at least one current language (e.g., Python, Matlab, C/C++).
• A proven record and interest in further developing modelling, programming and analytical skills.
• Strong affinity and enthusiasm for state-of-the-art deep learning methods and their medical applications.
• Demonstrated ability to be results oriented, applying creativity and out-of-the-box thinking to produce innovative concepts and solutions.
• Demonstrated proficiency in expressing yourself verbally and in writing in English, proven affinity for scientific writing.
• The ability to work in a team and take initiative.
• Affinity for teaching and guiding students.

To be a strong candidate for this position, it is nice - but not necessary - if you have:

• Background in medical physics or radiotherapy.
• Prior experience in artificial intelligence/machine learning/data science.
• Demonstrated experience with convolutional neural networks, generative adversarial networks, reinforcement learning methods and/or transformer architectures.
• Proven familiarity with modern deep learning libraries (PyTorch, TensorFlow, PyMC3, KERAS-RL).

You will receive a 5-year contract and will be deployed for AI-related education for the usual teaching effort for PhD candidates in the faculty plus an additional 20%. The extra year compared to the usual 4-year contract accommodates the 20% additional AI, Data and Digitalisation education related activities. All team members have many opportunities for self-development. You will be a member of the thriving TU Delft AI Lab community that fosters cross-fertilization between talents with different expertise and disciplines.

Delft University of Technology

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 and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact! 

Biomedical Intervention Optimization lab (BIOlab)

This position is connected to the Biomedical Intervention Optimization lab (BIOlab). BIOLab is a new TU Delft Artificial Intelligence Lab. Artificial Intelligence, Data and Digitalisation are becoming increasingly important when looking for answers to major scientific and societal challenges. In a TU Delft AI Lab, experts in ‘the fundamentals of AI technology’ along with experts in ‘AI challenges’ run a shared lab.

As a PhD, you will work with at least two academic members of staff and three other PhD candidates. In total TU Delft will establish 24 TU Delft AI Labs, where 48 Tenure Trackers and 96 PhD candidates will have the opportunity to push the boundaries of science using AI. Each team is driven by research questions which arise from scientific and societal challenges, and contribute to the development and execution of domain specific education.

Goal of the Biomedical Intervention Optimization lab (BIOlab)

Modern machine learning algorithms have achieved unprecedented accuracy in image and video understanding tasks by purely learning from data. These powerful abilities come at the price of enormous amounts of training data, memory and computational requirements. However, those resources are rarely available to real-time feedback systems in medical intervention and biomedical research.

The lab will focus on improving the efficiency of machine learning algorithms by designing novel artificial neural network architectures, developing new reinforcement learning and generative algorithms, and incorporating biologically-inspired neural network models. These newly developed concepts and algorithms will be applied to a wide range of problems in biomedical applications, such as optimizing tumor irradiation protocols with missing information, and in smart (super-resolution) microscopy to limit irradiation damage to delicate living samples.

With more than 1,000 employees, including 135 pioneering principal investigators, as well as a population of about 3,400 passionate students, the Faculty of Applied Sciences is an inspiring scientific ecosystem. Focusing on key enabling technologies, such as quantum- and nanotechnology, photonics, biotechnology, synthetic biology and materials for energy storage and conversion, our faculty aims to provide solutions to important problems of the 21st century.

To that end, we train students in broad Bachelor's and specialist Master's programmes with a strong research component. Our scientists conduct ground-breaking fundamental and applied research in the fields of Life and Health Science & Technology, Nanoscience, Chemical Engineering, Radiation Science & Technology, and Engineering Physics. We are also training the next generation of high school teachers and science communicators.

Click here to go to the website of the Faculty of Applied Sciences.