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Postdoctoral Research Associate in Deep Learning and Biomedical Image Analysis

London (Greater)
Grade 6: £41,386- £48,414 per annum including London Weighting Allowance depending on experience.

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Faculty Jobs
Health & Medical, Allied Health
Administrative Jobs
Academic Affairs, Research Staff & Technicians
Position Type
Employment Type
Full Time
Institution Type
Four-Year Institution

Job description

We are seeking a highly motivated and skilled Research Associate to join our team and contribute to cutting-edge research in computational pathology and biomedical image analysis. As a member of our team, you will be responsible for developing and implementing advanced deep learning techniques for classifying and segmenting multi-modal microscopy and spatial tissue image data.

The candidate should have strong background in deep learning and/or biomedical image analysis algorithm development and interest to employ such techniques to advanced applications in biology, drug development and precision medicine. The specific research project requires expertise in modern deep learning methods including graph neural networks, transformers, or recurrent neural networks. Experience in integration of disparate data types and data warehousing is desirable.

This post may appeal to candidates with background in computer science or engineering interested in now developing skills and experience in biomedical research. Candidates with good experience in machine learning, bioinformatics, database management, and visualisation techniques will also be considered. The successful candidate will be joining a new group and thus this position provides an opportunity for the right candidate to be part of an exciting new venture.

This is a highly collaborative project with several institutions including Oxford University, Cambridge University, and UCL.

This post will be offered on an a fixed-term contract for 2 years with the potential to be extended

Key responsibilities

  • Manage own research and administrative activities

  • Develop and apply deep learning algorithms for image analysis and classification of tissue data.

  • Work with large-scale image data sets, including whole slide H&E images, and multiplexed imaging data to extract meaningful features and insights.

  • Collaborate with other team members to develop and optimize data pre-processing pipelines.

  • Design and implement experiments to evaluate and validate the developed algorithms and trained deep learning models.

  • Contribute to building and maintain data management systems and data lakes.

  • Publish research findings in peer-reviewed scientific journals and present results at scientific meetings.

The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.

Skills, knowledge, and experience

The candidate should have a PhD degree in computational sciences, engineering, or bioinformatics. They should have good knowledge and experience in developing deep learning methods, and handling large-scale real world image data.

Essential criteria

1. Have PhD in Machine Learning, Computer Vision, Biomedical Engineering, Computer Science, Bioinformatics, Computational Biology, or another related area.

2. Excellent programming skills in Python.

3. Excellent communication skills, both written and oral, including the ability to write for publication, present research proposals and results, and represent the research group at meetings

4. Good understanding of software testing

5. Demonstrate a strong interest in interdisciplinary research.

6. Ability to manage own academic research and associated activities

7. Ability to contribute ideas for new research projects and research income generation

Desirable criteria

1. Experience in dealing with large image data and cloud computing.

2. Strong interest in biomedical applications.

3. Experience in large-scale image-based phenotyping in the wider sense.

4. Published research in a relevant field in high profile journals.

5. Experience of building modern database systems

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