PhD Student - CBCT-guided Adaptive Radiotherapy and Biological Monitoring in Lung Cancer

Radiotherapy is a primary treatment modality for patients with non-small cell lung cancer (NSCLC). However, managing treatment-related toxicity remains a significant challenge. Current clinical workflows are limited by the use of static treatment plans that do not account for daily anatomical variations or the evolving biological state of the tumor. Online adaptive radiotherapy (ART) addresses these limitations by allowing for plan adjustment while the patient is on the treatment table, utilizing daily in-room cone-beam CT (CBCT) imaging.

As a PhD candidate, you will focus on the development and clinical validation of novel online adaptive workflows. Your research will integrate artificial intelligence for image enhancement, the evaluation of novel radiotherapy schedules, and the use of liquid biopsies for biological monitoring. The objective is to transition toward highly individualized treatment strategies that improve both tumor control and patient safety.

Project Components

You will utilize Deep Learning systems, which enhance CBCT image quality, aiming for a level of accuracy sufficient for direct treatment adaptations. Based on these scans, we will create a "direct-to-LINAC" workflow. This bypasses traditional pre-treatment planning CTs, potentially improving treatment accuracy and efficiency. You will be responsible for analyzing data from clinical trials investigating a novel radiation protocol, "Primer Shot." This involves a single high-dose fraction followed by a three-week break designed to facilitate tumor reoxygenation. Finally, to complement anatomical adaptation, you will investigate biological markers of treatment response. This involves training and validating a liquid biopsy hypoxia signature. By analyzing protein and DNA/RNA panels in blood and correlating them with validated tissue signatures, you will assess the feasibility of non-invasively monitoring tumor oxygenation and response over a multi-week treatment course.

Your Responsibilities

• Test and validate AI models for CBCT image enhancement to support online adaptive adaptations.

• Design in-silico studies to evaluate the accuracy and robustness of CBCT-based plan adaptations.

• Clinically validate CBCT-guided online adaptations in a prospective trial for direct-to-treat radiotherapy.

• Analyze clinical data from the "Primer Shot" trials, including tumor response and toxicity outcomes.

• Train a liquid biopsy hypoxia signature and correlate this with clinical outcomes.

• Collaborate within a multidisciplinary team of medical physicists, radiation oncologists, and AI researchers at the Netherlands Cancer Institute (NKI).

• Present research findings at international conferences and publish in peer-reviewed medical physics and oncology journals.

You will be based at the Department of Radiation Oncology of the Netherlands Cancer Institute (NKI), embedded in an experienced, highly interdisciplinary team of medical physicists, physician-scientists, AI researchers and radiation technologists (RTTs).

Why the Netherlands Cancer Institute?

At the Netherlands Cancer Institute, we have a shared goal: providing the best care for every patient and every type of cancer. Quite a lot, but not impossible. Here, science and health care join forces towards innovation. We keep finding new ways to help people facing cancer on a global scale. Here we save lives, gain time and quality.

In the Department of Radiation Oncology, we are internationally recognized for image-guided and adaptive radiotherapy, including advanced CBCT- and MR-linac-based treatments, and for close collaboration between clinic, physics and data science.

• A master’s degree in Technical Medicine, Medicine, Biomedical Sciences, or another related field

• Strong affinity with medical image analysis

• A collaborative mindset, with the ability to work effectively across physics, biology, and clinical domains

• Excellent communication skills and proficiency in written and spoken English

• Experience with one or more of the following is a plus (not mandatory):

  • Analysis of Clinical trial data

  • Bioinformatics or the analysis of molecular biomarkers

  • Analyzing liquid biopsies

  • Knowledge of radiotherapy concepts (organs at risk, DVHs, TCP/NTCP, adaptive radiotherapy)

• Technical skills in programming (preferably Python) and an interest in machine learning/deep learning. 

Your development opportunities and employment conditions

The basis for your employment conditions is in accordance with the CLA Hospitals. You will receive from us:

• A fully funded 4-year PhD position at our top institution, with a dynamic and highly international workforce and ample opportunities for personal development. Preferred start date: September-October 2026;

• A contract for four years for 36 hours per week;

• A gross monthly salary between €3.813,- and €4.629,-, based on a 36-hour working week, in line with the PhD/OIO scale (in accordance with the CLA as of August 2026);

• 144 holiday hours and 57 hours of Personal Life Budget, with full-time employment;

• Annual holiday pay of 8.33% and a fixed end-of-year bonus of 8.33%;

• A travel allowance of €0.23 per km;

• Free parking at the Netherlands Cancer Institute. And with our bicycle plan and discount on public transport we also make it attractive for you to leave the car at home;

• Personal mentorship from experienced PIs and senior scientists in AI and radiotherapy, including clear milestones and a tailored career development plan;

• Access to large clinical datasets, GPU infrastructure, and best-in-class radiotherapy technology and imaging platforms;

• An active staff association and many opportunities to attend (inter)national conferences and courses;

• Opportunity to exercise at our Fit Boutiqs.

Interested?

We are curious about your talent! Will you be the one to make a difference in the world of cancer care and research? Apply now by using the apply / solliciteer button at the top of the page! Please attach your:

• CV, including projects, internships and, if applicable, publications or conference abstracts, and your grades from bachelor and master

• Cover letter, explaining your motivation for this position and how your background fits the project

• Contact details of 2 references (e.g. thesis supervisor, internship mentor)

You can upload only a limited number of files to the submission system, so make sure to merge your transcript of grades and CV into one PDF. Incomplete applications may not be considered.

Applications will be considered on a rolling basis until a suitable candidate is selected. For full consideration, please apply before Friday, May 15th, 23.59pm.

For further information about the position, please contact Dr. Zeno Gouw (z.gouw@nki.nl).

For questions about the application procedure, please contact Daphne Heemskerk (d.heemskerk@nki.nl).