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at national and international conferences. It is expected that there will be IP generated within the project. The successful candidate will have a PhD, or studying towards a PhD, in PSE with emphasis on machine
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to the pioneering translational work on biomarkers coupled to the non-endoscopic capsule technology developed by Rebecca Fitzgerald and her team. Applicants should have a PhD in experimental cancer biology and
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candidate will have a PhD in a strongly quantitative discipline, but applications from candidates close to submitting their PhD are also welcome. Familiarity with deep learning methodologies is essential, as
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quality data, and devising new project approaches. You will be expected to draft scientific manuscripts and may be involved with day-to-day supervision of Masters or PhD students. Candidates should have an
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October 2024 until 1 July 2025, and are non-renewable. Current PhD students should give their PhD Supervisor as their first referee. Fixed-term: The funds for this post are available for 9 months in
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teams, developing ideas and analysis collaboratively with other team members, and attending project meetings. Applicants should possess a PhD in health economics or a related discipline and be able
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contract of 9 months is offered with the possibility of an extension. Essential requirement 1: candidates must have (or be about to obtain) a PhD in materials science, chemistry, or chemical engineering
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approaches will be key. Outputs will include new estimation tools and inform the design of future population surveys, contributing to pandemic preparedness agenda. The successful candidate will have a PhD in a
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organisation skills and be detail-oriented, persistent, and passionate about working as a team to build a successful research group. Applicants must have a PhD degree in Bioinformatics or Computational Biology
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research group. Applicants must have a PhD degree in Biological Science with a focus on Microbiology or Immunology, preferably with some experience in murine in vivo infection and/or inflammation models