What is the iPhD programme?

The iPhD programme is designed to equip clinical academics with the expertise to tackle complex challenges by integrating cancer research with engineering and physical sciences. Tailored for exceptional undergraduate students enrolled in the MBBS/BSc degree course, this programme presents the unique opportunity to pursue an intercalated PhD alongside their studies. 


The programme is a pivotal component of the Cancer Research UK Clinical Academic Training Programme. This forward-thinking initiative is funded by the CRUK Convergence Science Centre, reflecting a strong commitment to advancing both medical and research frontiers. 


This pathway offers a distinct advantage to PhD supervisors. The collaboration between clinical academics and research experts not only enriches the training environment but also brings diverse insights to research projects. The unique combination of medical insight and research rigor can foster innovative problem-solving and lead to a more comprehensive understanding of complex medical challenges. The supervisor benefits from a motivated student whose dual expertise can propel research projects forward, opening new avenues for exploration and breakthroughs. 

 

In essence, the iPhD Pathway redefines academic training by seamlessly weaving together medical expertise and research acumen. By embracing this dynamic approach, students become not only skilled practitioners but also visionary thinkers driving advancements at the forefront of cancer research and its intricate connections with engineering and the physical sciences.

 

 

What is involved?

The PhD journey involves 3 years of intensive research, following the successful completion of the intercalated BSc (iBSc) during the fourth year of the MBBS degree. Upon finishing the PhD training, trainees will transition back into their undergraduate medical education for the fifth year. This dynamic approach ensures a comprehensive learning experience that bridges medical knowledge with cutting-edge research and innovation.


Timeline showing how intercalated phd fits in medical degree

What is available?

Support is available for 5 iPhD studentships for 2026. Studentships are typically 3 years in duration and provide:

  • a generous tax-free fixed stipend of £24,219 per year (for 3 years), subject to 1.75% indexation per year
  • PhD registration fees at the CRUK UK rate (£4,662 with 1.75% indexation per year) 
  • research running costs

 

You'll also have access to our Centre's training programme, available for all of our training programmes. See here for more information.

 

 

Eligibility

We welcome applications from students across all iBSc pathways, as long as they are committed to undertaking cancer research with a focus on convergence science.

 


How to apply 

1. Exploring project opportunities - October 2025

Imperial and ICR academics present summaries of their research projects. This is a chance to showcase the available research prospects for prospective students. The list of project summaries is available on this page every October.

 

2. Selecting Potential Projects - 20th November 2025

Students review these project summaries and choose three projects they're interested in pursuing for their potential PhD. Once the review is done, students need to send their top three preferences using the Candidate Preference Submission Form - iPhD 2026 along with their CV and Equal Opportunities Form - iPhD 2026 to icr-imperial-convergence.centre@imperial.ac.uk. Project supervisors are then informed of students' interest in their projects, leading to meetings to discuss the opportunities.

 

3. Submit final project choices - 19th January 2026

After initial student-supervisor meetings, students rank the three preferred projects from 1 to 3 and share the rankings. The chosen supervisor is informed, and if they agree, they are invited to submit a full proposal.

 

4. Developing Proposals - January - April 2026

During this partnership, a comprehensive PhD project proposal is developed. The proposal is reviewed by the training committee who assess its scientific quality and its fit for a 3 year PhD.

 

If a student doesn't secure their top project choice, their second choice becomes available for consideration.

 

5. Full PhD Proposal Deadline - April 2026

 

6. Panel Interviews - May 2026

Next, shortlisted students, along with their supervisory team, participate in interviews. These interviews serve to assess a) the project's quality, suitability, and feasibility for a PhD, b) the support the supervisory team offers, and c) the student's motivations for pursuing a PhD.

 

7. PhD Commences - July 2026

 

 

    Candidate Preference Submission Form - iPhD 2026
    Download  
    Equal Opportunities Form - iPhD 2026
    Download  

     

    Projects available for 2026

     

    Supervisors

    Dr Claire Fletcher (Imperial, Department of Surgery and Cancer) 

    Dr Nazila Kamaly (Imperial, Department of Chemistry)

    Proposed Outline

    Prostate cancer (PC) affects 1-in-8 UK men. Despite major advances in precision oncology, metastatic PC (mPC) remains incurable. Standard-of-care chemotherapies confer only modest survival benefits and are associated with systemic toxicity and resistance development. There is an urgent need for efficacious, tumour-selective and resistance-resilient therapies.


    This proposal uses prostate-targeted, biodegradable nanogels for intracellular delivery of therapeutic microRNAs (miRs). These act through novel mechanisms distinct from small-molecule therapies, and their combined use may prevent/delay resistance-onset and sensitise to DNA-repair/cell-cycle inhibitors:
    • miR-346: causes potent, genome-wide DNA damage and apoptosis in advanced PC cells; synergises with ATM/ATR/PARP/DNA-PKc inhibitors, induces tumour regression in vivo
    • miR-361-3p anti-sense-oligonucleotide (ASO-miR-361-3p, inhibitor of miR-361-3p): induces apoptosis, inhibits proliferation in vitro/in vivo in breast and PC
    • miR-16-5p/-424-5p: ‘master regulators’ of cell-cycle progression demonstrating potent synergy with clinical Wee-1/ATM/ATR/PARP inhibitors.
    The nanogel platform addresses key translational bottlenecks associated with conventional lipid nanoparticles (LNPs) and viral vectors: it is chemically-defined, modular, exhibits high cargo encapsulation-efficiency. Nanogels degrade selectively in cytosol in a glutathione-responsive manner and avoid endosomal retention, supporting precise release of therapeutic payloads whilst avoiding systemic off-target effects. Flexible chemistry enables integration of peptide/antibody-fragment ligands for precision delivery. We will target prostate-specific PSMA, using clinically-approved and in-lab validated ligand, PSMA-617. Crucially, this approach directly tackles therapeutic resistance and permits precision management of the most common cancer in men.

    We will:
    1. Encapsulate therapeutic miRs in PSMA-617-liganded nanogels using high-throughput Opentron-enabled nanogel library synthesis platform and perform comprehensive physicochemical characterisation.
    2. Evaluate nanogel-miR internalisation and therapeutic efficacy by assessing PC-relevant phenotypes (proliferation/apoptosis/cell-cycle/DNA damage) in cells lines modelling different PC stages
    3. Assess synergy of nanogel-miRs with standard-of-care therapeutics (chemotherapy/DNA repair inhibitors/AR pathway inhibitors)
    4. Evaluate nanogel-miR efficacy in patient-derived tumour tissues.

    We hypothesise that ‘programmable’ prostate-targeted nanogels can deliver potent miR therapeutics to PC tissues at high efficiency, providing proof-of-principle for future pre-clinical efficacy studies and clinical trials

     

     

    Supervisors

    Dr Prashant Srivastava (Imperial, National Heart & Lung Institute)

    Prof Guido Franzoso (Imperial, Department of Immunology and Inflammation)
    Prof Anguraj Sadanandam (ICR, Systems and Precision Cancer Medicine)
    Prof Jyoti Choudhary (ICR, Functional Proteomics)


    Proposed Outline

    The project will develop predictive biomarkers for patient stratification to translate the novel targeted therapeutic, DTP3, into healthcare benefit in MM. DTP3 is a first-in-class GADD45β/MKK7 inhibitor in Phase-2 clinical development (EudraCT:2021-004028-13), having produced strong objective clinical responses without toxicities as monotherapy in heavily pretreated MM patients. DTP3 selectively blocks NF-κB-dependent survival in MM cells via a novel mode of action that targets the essential cancer-specific module, GADD45β/MKK7, downstream of NF-κB, rather than NF-κB itself, thus avoiding the dose-limiting toxicities of conventional IKK/NF-κB-targeting drugs [PMID:25314077;PMID:25314072]. As such, DTP3 selectively kills MM and other cancer cells ex-vivo and in-vivo with no toxicity to normal tissues and no adverse effects [PMID:25314077;PMID:31080744]. Having established DTP3’s clinical efficacy and tolerability in the unmet need of MM, we aim to develop precision biomarkers to improve MM treatment through better diagnostics by integrating multi-omics data (transcriptomics, genomics/methylomics,  total/phospho-proteomics, scRNA-seq/scATAC-seq) from responders/non-responders in the current trial with similarly annotated public MM datasets [PMID:39160255;PMID:38942927].


    Our previous work demonstrated that elevated GADD45B (and MKK7) expression predicts DTP3 response in primary MM cells ex-vivo [PMID:30255568]. However, genome-wide studies have shown that single-gene alterations fail to capture tumour heterogeneity or reliably predict drug-response in patients. To address this, we will apply deep-learning neural networks integrating multi-omics and single-cell data, alongside unsupervised methods (clustering, NMF), to deconvolute MM heterogeneity and identify biological feature/functional disease states linked to responsiveness/resistance to DTP3. Publicly available multi-omics MM datasets [PMID:39160255;PMID:38942927;PMID:37081258] will be used for training to map GADD45β/MKK7 activity to biological pathways and MM subtypes for biomarker discovery. Datasets will be harmonized using multi-modal AI frameworks to derive robust, interpretable, and portable results. Integrative multi-omics data from patients in DTP3’s trial will be used for clinical validation. In addition to resolving phenotypic hallmarks of DTP3-sensitive myelomas, the integrative analysis of tumours that relapse after an initial response to DTP3 will clarify mechanisms of acquired resistance and expose co-vulnerabilities for combination-therapy selection. We will prospectively use this information to develop clinical-grade diagnostics for therapy-response prognostication, to inform the individual treatment strategy and deliver an effective precision medicine that benefits patients with MM and other hard-to-treat, NF-κB-driven cancers.

     

     

    Supervisors

    Prof Oscar Ces (Imperial, Department of Chemistry)

    Dr Adam Sharp (ICR, Translational Therapeutics)
    Prof Charlotte Bevan (Imperial, Department of Surgery and Cancer)
    Dr James Hindley (Imperial, Department of Chemistry)

     

    Proposed Outline

    Advanced prostate cancer (PCa) remains lethal, despite increases in overall survival afforded by the development of multiple new treatments including taxane chemotherapy, androgen receptor pathway inhibitors, PARP inhibitors and lutetium PSMA, due to eventual, inevitable treatment resistance. Thus, the most urgent unmet clinical need in inoperable, therapy resistant PCa is development of innovative treatment strategies with novel mechanisms of action that are effective and well-tolerated. Current therapies are associated with undesirable side-effects and systemic toxicities which decrease compliance and adversely affect patients’ quality of life. We have identified novel anti-apoptotic treatment strategies targeting the MCL1 alone, or in synergistic combinations, that drive cancer specific cell death in lethal PCa. They have toxicity profiles that make systemic delivery challenging. We propose to address this by loading drugs into “synthetic cells” that can respond specifically to the prostate tumour microenvironment to release their payload in a targeted, tumour cell-specific manner.

     

    This project aims to develop microfluidic production pipelines containing in-line measurement of particle assembly and drug loading, enabling machine-guided optimisation of formulation synthesis. We will use this pipeline to (i) generate vesicle-based synthetic cell libraries with controlled lipid, protein and drug composition, building on previous work demonstrating that synthetic cells can respond to specific enzyme combinations in the prostate cancer microenvironment ,  and (ii) optimise these for delivery of new, targeted prostate cancer therapeutics that inhibit anti-apoptotic proteins, namely MCL1 and/or BCLXL/BCL2 (dual inhibitor). Targeting anti-apoptotic proteins is an attractive therapeutic strategy for lethal, castration-resistant prostate cancer for which no curative options exist.

     

     

    Supervisors

    Dr Mitchell Chen (Imperial, Department of Surgery and Cancer)

    Prof Eric Aboagye (Imperial, Department of Surgery and Cancer)


    Proposed Outline

    Precision oncology is transforming cancer treatment by moving from uniform protocols to individualised strategies. Non-small cell lung cancer (NSCLC), the leading cause of cancer-related mortality worldwide, illustrates this paradigm shift. Checkpoint blockade immunotherapy (CBI) has improved survival in selected patients, yet identifying responders remains difficult due to the limitations of the currently used PD-L1 testing. Although the use of AI on imaging data is being utilised to address this important clinical topic, most currently available computational tools for precision NSCLC CBI are often poorly explainable, hindering their clinical adoption. Foundation models, large-scale machine learning systems pretrained on multimodal biomedical data, offer a promising avenue to bridge these gaps by integrating diverse data sources and generating biologically meaningful, clinically actionable predictions. These models hold potential for guiding CBI patient selection and identifying individuals at high risk of relapse who may require closer monitoring.

    Aims/Objectives
    This PhD project will develop and validate a multimodal foundation model to guide precision CBI in NSCLC. Specific objectives are:

    1. Discover robust biomarkers of therapeutic response, resistance, and prognosis through self-supervised learning.
    2. Develop a multimodal foundation model that integrates genomic, histopathologic, imaging, and clinical data.
    3. Enhance model explainability using attention mechanisms, causal inference, and domain-informed constraints.
    4. Validate the model for outcome prediction and treatment decision support.

    Preliminary Works
    Our group have already developed imaging biomarkers predictive of CBI response and frameworks for deep image–molecular data integration, including via variational autoencoder and contrastive learning. We have also completed a pilot study using single-cell spatial transcriptomics profiling to reveal tumour-associated macrophages and mediators such as SPP1, MARCO, and CXCL9 as key regulators of CBI resistance. These findings provide a strong foundation for undertaking the proposed multi-modal AI development.

    Convergence Approach
    The supervisory team combines expertise in radiology, machine learning, cancer biology, and causal modelling. The project will integrate transcriptomics, pathology, radiology (CT, FDG-PET), and clinical datasets using contrastive learning and/or variational autoencoder approach. Foundation models will be pretrained on multimodal NSCLC dataset, and tested on UK and international datasets.

    This studentship offers a unique opportunity to join a highly multidisciplinary cancer research team and work at the interface of oncology, machine learning, and cancer biology, developing cutting-edge AI tools with direct translational potential in precision cancer care

     

    Supervisors

    Prof Dennis Wang (Imperial, National Heart & Lung Institute)

    Prof Paul Huang (ICR, Molecular and Systems Oncology)


    Proposed Outline

    This project focuses on improving how we design drug combination therapies, which are often more effective and safer than single drugs. Current AI-based models can predict which drugs might work well together, but they don’t tell us how confident we should be in those predictions, nor do they consider the order in which drugs are given, a factor that can dramatically change outcomes in real patients. By combining statistics, computer science, and clinical knowledge, this research will build new AI tools that address both uncertainty and drug sequencing. The goal is to provide clinicians and researchers with practical guidance for selecting and testing drug combinations, ultimately speeding up the path to better treatments for conditions such as cancer and heart disease. The PhD student will play a central role in developing and applying new AI methods to improve drug combination design. They will begin by curating and building the first large-scale datasets of drug combinations that include information on dosing sequence and timing, systematically mining resources such as DrugCombDB and published studies. Using these datasets and the machine learning expertise in Prof Dennis Wang’s team, the student will extend statistical inference frameworks to decompose drug synergy into potency, efficacy, and viability while explicitly quantifying uncertainty in predictions. They will also design and implement deep learning models capable of capturing the temporal effects of drug administration order, integrating multimodal data such as molecular and pathway features. Working closely with biomedical collaborators, the student will test predictions in relevant cancer models, including sarcoma organoids developed in the Prof Paul Huang’s lab. Overall, the student will gain skills in statistical modelling, AI development, data curation, and translational validation in oncology.

    Supervisors

    Dr Ehsan Ghorani (Imperial, Department of Surgery & Cancer)

    Prof Sarah Filippi (Imperial, Department of Mathematics)


    Proposed Outline

    Immunotherapy has transformed cancer care, yet only a minority of patients benefit, and clinicians currently lack a simple, reliable biomarker to predict who will respond. Every patient undergoing treatment already has blood samples analysed by automated haematology machines, which measure detailed single-cell morphology features for thousands of leucocytes per test. These rich data are routinely discarded after basic cell counts are reported.

    DeepMorph is an interdisciplinary project that will harness these underused data to develop artificial-intelligence models capable of predicting and monitoring immunotherapy response from standard blood tests. The project brings together expertise in oncology, immunology and statistical machine learning.

    The student will build upon a large-scale database of full blood count morphology data linked to clinical outcomes in patients receiving anti-PD-1 therapy on the phase III REFINE-Lung clinical trial. Using this resource, they will develop deep-learning frameworks that treat each blood sample as a collection of individual cells. Transformer-based neural networks and attention-based learning methods will be used to identify subtle patterns of immune activation and interaction between cell types that distinguish treatment responders from non-responders.

    A key focus will be explainability that is a critical element of AI applied to healthcare: the project aims to highlight which morphological features and cell populations drive its predictions. These candidate populations will then be isolated from blood using magnetic bead and flow-cytometric sorting and characterised in the laboratory through high-dimensional immunophenotyping. This loop between computational discovery and experimental validation will reveal how specific immune states manifest in cell morphology and how these reflect treatment efficacy.

    By combining routine diagnostic haematology data with cutting-edge deep learning and immunology, the project aims to create a low-cost, scalable biomarker to personalise immunotherapy, improve patient outcomes, and reduce unnecessary treatment toxicity. The project provides a unique training opportunity at the interface of medicine, data science, and experimental immunology.

     

     

     

    Further Information

    For more information on this opportunity send an email to icr-imperial-convergence.centre@imperial.ac.uk.

     

    Explore other training programmes

     

     

     

     

    Accessibility Cookie policy Privacy policy