AI-Driven Cancer Imaging Research Fellow in Manchester

AI-Driven Cancer Imaging Research Fellow in Manchester

Manchester Full-Time 29700 - 36300 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Develop AI methods for analysing cancer imaging and clinical data.
  • Company: The University of Manchester, a leader in cancer research.
  • Benefits: Competitive salary, access to cutting-edge technology, and collaborative research environment.
  • Other info: Join a dynamic team dedicated to advancing cancer sciences.
  • Why this job: Make a real difference in cancer diagnosis and prognosis through innovative research.
  • Qualifications: Experience in computational methods and a passion for cancer research.

The predicted salary is between 29700 - 36300 £ per year.

The University of Manchester is seeking a Research Associate or Research Fellow to contribute to an ambitious cancer sciences programme, focusing on digital pathology, AI, and data-driven discovery. You will develop computational methods to analyze large-scale imaging and clinical data, working with clinicians and researchers in a collaborative setting.

Responsibilities include:

  • Designing analytical pipelines
  • Integrating datasets
  • Interpreting results to improve cancer diagnosis and prognosis

AI-Driven Cancer Imaging Research Fellow in Manchester employer: RFCSR

At King’s College London, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. Our interdisciplinary approach not only encourages professional growth through diverse research opportunities but also allows you to make a meaningful impact in the fields of engineering and biomedical research. Located in the vibrant city of London, our institution offers a dynamic environment where creativity thrives and employees are supported in their career development.

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Contact Details:

RFCSR Recruitment Team

We think you need these skills to ace AI-Driven Cancer Imaging Research Fellow in Manchester

Computational Methods Development
Data Analysis
Digital Pathology
AI and Machine Learning
Analytical Pipeline Design
Dataset Integration
Clinical Data Interpretation