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seek an ambitious and self-motivated postdoctoral researcher with a strong background in bioinformatics and/or computer sciences (or related fields). Experience with AI and Machine Learning models in
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the acquisition and analysis of (clinical) MRI data, as well as with mathematical modelling, image processing methods and/or machine learning. Experience of methods used for quantitative MRI and/or previous hand-on
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experience: Essential criteria PhD awarded in Computer Science, Machine Learning, Biomedical Engineering, Mathematics, or a related subject area * First or Second-Class Honors in mathematics, physics
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To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD awarded in Computer Science, Machine Learning, Biomedical Engineering
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practice impact while also learning about machine learning techniques. The successful applicant will work in the newly formed Health Equity Evidence Centre and innovative new centre that will generate data
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) at Northeastern University London (NUL) is looking for an exceptional candidate to fill an open Postdoctoral Research Associate position in Network Science, Graph Machine Learning, and Cognitive & Social Sciences
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Department of Computer ScienceLocation Egham Salary £40,839 to £48,003 per annum - including London Allowance Post Type Full Time Closing Date 23.59 hours GMT on Sunday 23 February 2025 Reference
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experience with the acquisition and analysis of (clinical) MRI data, as well as with mathematical modelling, image processing methods and/or machine learning. Experience of methods used for quantitative MRI
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mathematical modelling, image processing methods and/or machine learning. Experience of methods used for quantitative MRI and/or previous hand-on experience implementing MRI pulse sequences are a plus. Ideally
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Demonstrable comprehensive knowledge in the subject area Ability to use key AI and/or Machine Learning software, e.g., Python, Java, C++, Prolog, Javascript Proven ability to analyse and write up research