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Research Fellow - Institute of Cancer and Genomic Sciences - 104098 - Grade 7


Job details
  • University of Birmingham
  • 6 months ago
Applications closed

Summary

This post-doctoral research will be conducted in collaboration with a multidisciplinary team of experts in the fields of neuroscience, immunology, pain, anesthesia, and machine learning. The team also aims to recruit additional post-doctoral research associates, one research technician, one research assistant, and one research nurse.

Supervision will be provided by Dr. Andreas Karwath , and Dr. Ali Mazaheri, as well as Prof. Fang Gao Smith, and Dr. Helen McGettrick ,

The successful candidate will have a strong background in computer science, state-of the-art machine learning, artificial intelligence and multi-modal data integration. Experience with applying explainable machine learning/AI approaches within a medical or clinical setting, in particular in combining different modalities (EHR, biomarkers, longitudinal data, multi-omics, EEGs, NLP, , is a clear advantage. 

In your online application please include detail on how you match the person specification.

Main Duties

The responsibilities may include some but not all of the responsibilities outlined below.

Developing novel computer-based models, techniques and methods  Develop research objectives and proposals for own or joint research, with assistance of a mentor if required Contribute to writing bids for research funding Analyse and interpret data Apply knowledge in a way which develops new intellectual understanding Disseminate research findings for publication, research seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Undertake management/administration arising from research Contribute to Departmental/School research-related activities and research-related administration Contribute to enterprise, business development and/or public engagement activities of manifest benefit to the College and the University, often under supervision of a project leader Collect research data; this may be through a variety of research methods, such as scientific experimentation, literature reviews, and research interviews  Present research outputs, including drafting academic publications or parts thereof, for example at seminars and as posters  Provide guidance, as required, to support staff and any students who may be assisting with the research  Deal with problems that may affect the achievement of research objectives and deadlines Promotes equality and values diversity acting as a role model and fostering an inclusive working culture.

Person Specification

A PhD (or one near to completion) in the areas of (Health) Data Science, Computer Science, Artificial Intelligence, or related discipline. Candidates with a PhD from the healthcare area with excellent and proven ML/AI & computational skills are encouraged to apply.  Demonstrable knowledge of developing cutting-edge machine learning/AI methods applied to clinical and medical challenges with additional focus on multi-modal data integration, cross-sectional & longitudinal data and explainable AI.  Excellent programming skills in Python and practical knowledge of current machine learning and deep learning libraries and frameworks (NumPy, SciPy, Pandas, scikit-learn, TensorFlow, PyTorch, Hugging Face, etc. ). Experience in High Performance Computing and Linux-based systems . High level analytical capability Ability to disseminate research findings through publication and/or oral presentations.  Ability to communicate effectively with colleagues from different academic disciplines and deliver information clearly. Self-directed, flexible approach to work.  Contribute to the planning and organising of the research programme and/or specific research project Co-ordinate own work with others to avoid conflict or duplication of effort. Knowledge of the protected characteristics of the Equality Act 2010, and how to actively ensure in day-to-day activity in own area that those with protected characteristics are treated equally and fairly

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