Research Software Engineer in Deep Learning and Bioinformatics - Tenders Global

Research Software Engineer in Deep Learning and Bioinformatics

King's College London

Job id: 081870. Salary: £37,332 – £41,024 per annum, including London Weighting Allowance.

Posted: 03 January 2024. Closing date: 30 January 2024.

Business unit: Faculty of Life Sciences & Medicine. Department: Institute of Pharmaceutical Science.

Contact details:Dr Heba Sailem. [email protected]

Location: Waterloo Campus. Category: Research.

Job description

​We are seeking a highly motivated and skilled Research Assistant to join the Biomedical AI and Data Science group led by Dr Sailem ( ). You will contribute to cutting-edge research in machine and deep learning applied to precision medicine. As part of our team, you will play a key role in developing and implementing advanced deep learning techniques focused on identifying novel therapeutic targets and optimizing drug structures.

​The candidate should have strong background in deep learning and have experience in modern deep learning models including graph networks, transformers, GANs, and recommender systems. Expertise in biomedical image analysis, bioinformatics and multi-omics development and interest to employ such techniques to advanced applications in biology, drug development and precision medicine. The ideal candidate should possess excellent interpersonal skills, demonstrating a collaborative and cooperative approach towards teamwork.

This post may appeal to candidates with background in computer science or engineering and interested in now developing skills and experience in biomedical research. Candidates with good experience in bioinformatics, database management, and visualisation techniques will also be considered.

​This is a highly collaborative project with several institutions including Oxford University, Cambridge University, and UCL as part of TransNAT consortium – .

This post will be offered on a fixed-term​ contract for ​18 months with the potential to be extended​.

This post is full time  – 100% full time equivalent.

Key responsibilities

  • Manage own research and administrative activities.
  • ​Develop and apply deep learning algorithms for biomedical 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 first 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 data.

Essential criteria

  • Have a first degree in Machine Learning, Computer Vision, Biomedical Engineering, Computer Science, Bioinformatics, Computational Biology, or another related area.
  • ​Excellent programming skills in Python.
  • 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.
  • Good understanding of software testing.
  • Demonstrate a strong interest in interdisciplinary research.
  • Ability to manage own academic research and associated activities.
  • ​Ability to contribute ideas for new research projects and research income generation.
  • Desirable criteria

  • Experience in dealing with large dataset and cloud computing.
  • Strong interest in biomedical applications.
  • Experience in large-scale image-based phenotyping in the wider sense.
  • Published research in a relevant field in high profile journals.
  • ​Experience of building modern database systems
  • ​​

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