RadNet – our radiation research network

RadNet header art

As RadNet develops, there will be opportunities for researchers, research organisations and industry.

Get in touch

CRUK RadNet is a network of centres of excellence and state-of-the-art facilities working with the research community to tackle the major challenges in radiobiology and radiation oncology. 

So far, we’ve invested ~42.3M in the RadNet centres of excellence to establish a critical mass of activity, expertise and infrastructure in multiples locations.  

We’re continuing to build a portfolio of internationally leading research in radiation biology and radiation oncology, with the goal to attract further funding for radiation research from research funders and institutions.

In our most recent research strategy, we commit to maintaining our momentum in this area as a key research priority. We’ll increase our understanding of the underpinning biological mechanisms and how best to apply and deliver radiotherapy.

Centres of excellence funding

We’re now prioritising up to £20m of additional strategic funding to enable a second phase of RadNet. We’ll aim to support four to five centres of excellence in radiation research for the next five years through to 2029.  

The funding will enable centres of excellence to: 

Establish and maintain cutting-edge research infrastructure to enable innovative radiation research. 

Facilitate both forward and reverse translation through active engagement with world-leading discovery, translational, and clinical research communities. 

Support and train the radiation researchers of the future in an enabling and supportive environment. 

Applicants can request further support for engagement with the local research community and for early-career researcher training, enabling novel strands of radiation research. This should support posts within established discovery and translation research groups locally, such as PhD studentships and early career fellows or postdoctoral researchers. 

Contact us by 20 February 2024 if you’re interested in applying. The full application deadline is on 27 February 2024. 

The review panel and interviews of applicants will take place on 2-3 May 2024, with funding for successful application issued in autumn 2024. 

Opportunities to get involved

Whether you’re a cancer biologist, health professional, engineer or physical scientist, RadNet is an exciting and rewarding opportunity to apply your expertise and knowledge, even if you’ve not worked in the field of radiation research before.

New opportunities for researchers collaborating with RadNet centres include:

  • Access to expertise, facilities, platforms and technologies
  • Seed funding for new cross-site translational research projects
  • Protected research time for clinicians and allied health professionals
  • PhD studentships, postdoctoral research posts and clinical fellowships with the centres

Opportunities through established funding routes include:

The scientific opportunity

Our research strategy identified the key role of radiotherapy in achieving our ambition of 3 in 4 patients surviving their cancer by 2034. To drive this ambition we are capitalising on technological advances and infrastructure investments to create opportunities to tackle the key scientific challenges, including

  • Biological response of tumour and non-malignant cells to ionising radiation
  • Signatures of cellular damage induced by alternative particle and photon radiation sources
  • Inherent radio-resistance or sensitivity of different tumour types/subtypes
  • Evolution of radio-resistance to radiotherapy and tolerance to DNA damage
  • Radiation response at the tissue level and the influence of the of stroma
  • Contribution of immune system to radiotherapy response
  • Novel preclinical models to study radiotherapy response, resistance and toxicity 
  • Novel opportunities to exploit synthetic lethality through therapeutic combinations
  • Predictive biomarkers to enable stratification and personalisation of radio- or combination therapy
  • Surrogate markers of late toxicity from radiotherapy, and potential mitigation strategies
  • Imaging approaches and intra-therapy biomarkers to enable adaptive treatment planning
  • Innovative clinical trial design with sufficient power for biomarker endpoints
  • Surrogate markers of late toxicity from radiotherapy, and potential mitigation strategies
  • Hypothesis driven combination trials of novel therapeutics with radiotherapy modalities

Our network

Each location has its own research priorities that build on local strengths. These represent an ideal opportunity to partner with them in shared areas of interest.

Cambridge Radnet Centre

CRUK RadNet Cambridge

  • DNA damage response and resistance
  • Defining drug-radiation combinations
  • Developing clinically relevant models
  • Translation-rich neo-adjuvant trials
  • Radiogenomics and radiomics
Photograph of a radiographer working in a control room

CRUK RadNet City of London

  • Radiation resistance
  • Radiation combinations
  • Targeting and technology
  • Cross-cutting: Outcomes and risks
  • Cross-cutting: Clinical translation
Glasgow Radnet Centre

CRUK RadNet Glasgow

  • Tumour biology and radiation response
  • Development of preclinical models
  • Preclinical and clinical imaging
  • Clinical radiotherapy research
Photograph of a model of a radiotherapy table in a control room.


  • Molecular responses to DNA damage
  • Radiation-induced immune responses
  • Translational and clinical research
Leeds Radnet Centre

CRUK RadNet Leeds

  • Personalised and adaptive radiotherapy
  • Re-irradiation
  • Combining radiotherapy with novel therapies
Photograph of a radiotherapy patient and healthcare professional

CRUK RadNet Manchester

  • Immunological effect of radiotherapy
  • Treating complex comorbid patients
  • Tumour microenvironment and genetic instability
  • Cross cutting: Proton vs photon
  • Cross cutting: FLASH radiotherapy
  • Cross cutting: Biomarkers

CRUK RadNet Oxford

  • Radiotherapy and the immune response
  • Imaging and oxygen consumption
  • Ultra-fast delivery of irradiation
  • AI and machine learning