ACED PhD Studentship 2028
4-year ACED PhD programme in Cancer Early Detection at UCL.
Applications are now closed. The next campaign for PhD studentship will open in the summer 2027.
Background Information
The International Alliance for Cancer Early Detection (ACED) is a partnership between Cancer Research UK, University College London, Dana Farber Cancer Institute, The University of Manchester, the German Cancer Research Center (DKFZ), University College London, Knight Cancer Institute at OHSU and The University of Cambridge.
Earlier detection of cancer offers the greatest opportunity to deliver improvements in successful outcomes for patients. By diagnosing cancer earlier, it is easier to treat, causing less suffering to patients and saving lives.
ACED has the bold ambition to accelerate and revolutionise research in the early detection of cancers by uniting world-leading researchers to bring together the best early detection science across the UK and US.
We are seeking candidates with an outstanding academic record with a minimum upper second class or Master’s degree or equivalent in a discipline relevant to early detection research, including but not limited to: biochemistry/molecular biology, biophysics, computer science, engineering, epidemiology, public health, physics, mathematics, medicine.
Details of the studentship
ACED is committed to training the next generation of early detection scientists and providing a supportive and flexible training environment. The ACED PhD programme provides unique support by offering a multi-disciplinary and multi-institutional approach to training.
The first year of the Programme allows the students to develop their project with their chosen UCL supervisor as well as training in different disciplines relevant to cancer early detection. This will be followed by a 3-year research project.
ACED PhD students will also benefit from:
- Opportunities for rotations in different ACED Centres in the first year.
- Dedicated ACED Early Detection of Cancer training programme.
- Early Detection Summer School.
- Annual Early Detection of Cancer Conference.
- UCL Doctoral Skills Development Programme providing the opportunity to expand your research and transferable skills in order to support your research, professional development and employability.
Applicants must demonstrate a strong interest in early detection of cancer research. Applicants may only submit ONE application for an ACED PhD Studentship.
Funding
The funding for this studentship includes tuition fees at the UK home rate only (funding will not cover international fees). For eligible students, the studentship will provide tuition fees, running expenses and an annual maintenance stipend starting at £24,219 funded by Cancer Research UK for up to 4 years, subject to satisfactory progress.
Eligibility
Due to funding restrictions, we will only be able to offer studentships to candidates that have UK “home” tuition fee status (i.e. UK National or have EU “pre-settled” or “settled” status), self-funding of the international portion of the tuition fee will not be eligible. For more information on home tuition fee status please visit the UKCISA website. Candidates must meet the UCL entry requirements.
Supervisor arrangements
Successful students will select their Principal Supervisor in a subject area of their choice from a list of approved UCL ACED PhD Supervisors
The Principal Supervisor and student will co-develop the PhD research project during the first year of the Programme, including selecting a suitable co-supervisor with complementary expertise from another ACED Member Centre.
UCL ACED PhD Supervisors
| Name | Research Interests in early detection | Research question to form the basis of PhD project |
|---|---|---|
| My laboratory investigates how host metabolism and its perturbation by obesity contribute to tumour development and response to therapy. We have recently discovered that tumours of various origins cause early systemic changes in host metabolism that promote tumorigenesis by suppressing anti-tumour immunity. We are, therefore, exploring ways to leverage these findings towards improved cancer prevention, detection and treatment. | How do early tumour-induced changes in host metabolism influence systemic immunity and how can we use this information for timely cancer detection? | |
| Early detection of women’s cancers, both large-scale population trials and discovery of novel biomarkers. More recent research into inequalities in endometrial cancer. | How can innovations in early detection, both in biomarkers and imaging, advance early detection of ovarian, endometrial, and cervical cancer - with relevance both to high and low-to-middle income settings? | |
| My laboratory investigates how the immune response to viral infection contributes to lung cancer funded by the UKRI. We have active projects on how the antiviral cytosine deaminase enzyme APOBEC3 induces DNA damage contributing to lung tumour initiation. | Do APOBEC3B (A3B) enzymes induce breakage fusion bridge (BFB) cycles amplifying oncogenic mutations in lung cancer? How can we use cancer cell line models with or without APOBEC3B (A3B) expression to determine if oncogenic mutations are amplified by A3B enzyme activity driving BFB formation? | |
| Apply machine learning and computational methods for medical imaging and surgical applications. | To what extent can machine learning and artificial intelligence improve early prediction of cancer occurence, recurrence and progression compared to current clinical methods? | |
| Early detection: Initiation and evolution of high-grade serous ovarian cancer; New strategies for risk stratification, identification of pre-cancerous disease and early interception of tumour initiation/progression. | What are the molecular mechanisms and the cellular context associated with initiation of high-grade serous ovarian cancer? How do epithelial cells interact with the innate immune system in order to increase their fitness? | |
| Cancer is a genetic disease. How (epi)genetic mutations drive the clonal expansion and progression of commonly diagnosed precursor lesions remains poorly understood, which impacts our ability to risk stratify patients with precancerous lesions for surveillance intensity and future cancer risk. My team studies precancerous lesions across the gastrointestinal tract to develop better detection and surveillance strategies. | Patients with familiar cancer syndromes such as Lynch syndrome are at increased cancer development risk and undergo surveillance from a young age to detect cancer initiation early. The evolution and natural history of these precursor lesions is unclear. How can we map the molecular immunophenotype of precursor lesions to better understand the interaction of precancerous lesions with a patient’s immune system? | |
| The interplay between genome stability maintenance and immune surveillance is crucial for preventing cancer initiation. We aim to investigate the molecular mechanisms of this interplay, with the focus on women’s cancers. These studies will provide a springboard for translational advances that benefit patients and individuals at risk of developing disease. | How do cells with genomic instability survive and evade immune surveillance to progress into precancerous lesions, and can we develop strategies to reactivate these pathways using genetic/pharmacological screens in stem cell-derived organoid models of ovarian cancer initiation? | |
Epidemiology of Cancer Healthcare and Outcomes (ECHO), Behavioural Science and Health, Institute of Epidemiology and Health Care | ECHO is a multi-disciplinary Group established in 2015 (currently including 12 researchers). We aim to help control the increasing burden of neoplastic disease through research by improving how patients with cancer are diagnosed and treated. | How can epidemiological studies using population-based data on measures and markers of early diagnosis and related inequalities; patient trajectory analysis of healthcare events (routes) to diagnosis and related inequalities reveal mechanisms and opportunities to improve care; risk stratification / assessment of cancer risk in symptomatic patients presenting with non-specific symptoms? |
| My laboratory uses a combination of human studies and zebrafish molecular genetics to study clonal haematopoiesis of indeterminate potential (CHIP) and its evolution to haematological malignancies. Our goal is to define novel clinical modifiers of CHIP alongside basic biological understanding about the key pathways underpinning the factors leading to evolution of blood cancers as a result of these premalignant clones of cells that are found routinely in the blood of older patients. Our current projects have identified a role for statins as able to modify the rate of expansion of CHIP clones. We will be developing additional projects testing this in vivo in our zebrafish models and in vitro assessing the mechanisms by which statins may impact clone fitness. | How do lipid lowering drugs affect the fitness of Clonal haematopoiesis clones and evolution to haematological malignancy? | |
| My research is dedicated to developing advanced computational techniques for quantitative MRI, with a focus on establishing validated, non-invasive imaging biomarkers to replace traditional tissue biopsies in cancer diagnostics. A key achievement is VERDICT-MRI, which enables in vivo characterisation of tumour microstructure and is undergoing clinical trials, marking a significant step toward integrating quantitative MRI into routine oncological care. | Integrating imaging with digital pathology aims to develop predictive, explainable tools that identify tumour subtypes and forecast which early-stage patients will benefit from adjuvant therapy—advancing precision oncology and reducing unnecessary treatment. How can we use multi-modal deep learning and histology-informed AI to analyse quantitative MRI for early cancer detection? | |
| We use tumour antigen specific T cell omics to track and target lung cancer development, leveraging globally unique lung cancer screening cohorts comprising > 10,000 individuals. | We have discovered novel T cell specific blood signatures which distinguish CT screened smokers with vs without Stage 1 lung adenocarcinoma.This project will address the following questions: - How can we use machine-learning/AI to develop the most specific and sensitive T cell-driven test that predicts who will get lung cancer? - How can we integrate T cell and radionic features such as growth rate to forecast rapid lung cancer progression during screening? - Which patients are most likely to benefit from immune interception or early neoadjuvant immunotherapy to stop lung cancer formation at its earliest stage? | |
| We create digital twins using AI and computational modelling that can be combined with imaging physics to identify new biomarkers of early cancers. | How can AI combined with imaging and mathematical modelling enable us to detect cancer earlier? What are the earliest signatures of cancer we can detect in clinical imaging data? | |
Targeted Intervention, Division of Surgery and Interventional Science | Developing more accurate ways to diagnose and treat cancers by combining modalities such as pathology and imaging or molecular modelling and wet lab validation. | How can we develop drugs to reduce the risk of developing cancer or to intercept cancer development at its earliest stages to prevent aggressive cancers forming? |
Contact
If you have any queries about this studentship or the application process, please contact Dr Dan Kelberman, UCL ACED Programme Manager.
