Senior Lecturer in Machine Learning Analysis with Expertise in Bioimaging and Metadata
THE UNIVERSITY OF ABERDEEN
SCHOOL OF MEDICINE, MEDICAL SCIENCES AND NUTRITION
SENIOR LECTURER IN MACHINE LEARNING ANALYSIS WITH EXPERTISE IN BIOIMAGING AND METADATA
REF NO: IMS151A
We are seeking to recruit an individual with a strong research portfolio around the analysis of biomedical imaging data. The individual’s research activities will preferably be in the field of machine learning / artificial intelligence, focussed on medical imaging data and the analysis and exploitation of big data within biomedicine.
The successful candidate will be based within the Institute of Medical Sciences (IMS) (http://www.abdn.ac.uk/ims/), specifically in the Aberdeen Biomedical Imaging Centre (ABIC)
(https://www.abdn.ac.uk/ims/research/abic/), which brings together clinical and basic science researchers investigating imaging technology, image analysis and applications.
Candidates must have a PhD in data science or biomedical imaging with a proven ability to attract regular funding and publish papers in high quality journals. Experience of teaching at undergraduate and/or postgraduate level including research student supervision is required.
We wish to appoint an excellent scientist with a strong academic track record in biomedical imaging data analysis, ideally in machine learning / computational neural networks / artificial intelligence. They will have a record of delivering high quality scientific output and publications, building an externally funded research team and creating a vibrant environment for research and teaching. The successful applicant will establish fruitful partnerships with colleagues within the Aberdeen Biomedical Imaging Centre, the Institute of Medical Sciences, the School of Medicine, Medical Sciences and Nutrition, the wider University and industry partners.
A contribution to the teaching portfolio within the School is required and candidates should demonstrate excellence in education and supervision at undergraduate and/or postgraduate levels.
Public engagement with research is embedded within the IMS and is an important objective of the School, therefore appointees will be expected to contribute and expand the current portfolio.
The successful candidate will have a track record of bringing in external funding and will develop partnerships across academia, healthcare and industry. In collaboration with the Aberdeen Centre for Health Data Science, the successful candidate will develop AI solutions for image and metadata analysis that address current healthcare priorities, such as our ageing population, chronic diseases and workforce shortages in the NHS.
Salary will be at the appropriate point on the Grade 8 salary scale (£51,630 - £58,089 per annum), and negotiable with placement according to qualifications and experience.
Any appointment will be made subject to satisfactory references and a 3 year probation period.
For further information on various staff benefits and policies please visit www.abdn.ac.uk/staffnet/working-here
Should you require a visa to undertake paid employment in the UK you will be required to fulfil the minimum points criteria to be granted a Certificate of Sponsorship and Tier 2 visa. As appropriate, at the time an offer of appointment is made you will be asked to demonstrate that you fulfil the criteria in respect of financial maintenance and competency in English. Please do not hesitate to contact Grant Rae, HR Adviser on +44 (0)1224 437068 or email firstname.lastname@example.org for further information.
The closing date for receipt of applications is 12 May 2019
Should you wish to make an informal enquiry please contact:
Professor Paul Fowler, Interim Head of School of Medicine, Medical Sciences and Nutrition
Professor David Lurie, Chair in Biomedical Physics & Bio Engineering
Please do not send application forms or CVs to Professors Fowler or Lurie.
Please quote reference number IMS151A on all correspondence
The University pursues a policy of equal opportunities in the appointment and promotion of staff.
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