29 data-"https:"-"https:"-"https:"-"UCL"-"UCL" Fellowship positions at University of Nottingham
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) and is available as a fixed term contract until 01/02/2027, with the possibility for extension. Further information is available in the role profile. To apply for this vacancy please click ‘Apply Now
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partner institutions to ensure alignment of experimental design, data generation and interpretation across work packages. In addition to contributing to consortium-wide objectives, the role holder will have
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approximately 5-10°C warmer than the 1970-2010 average. The role will involve the generation of ecophysiological data as measurements of plant performance and generating morphological and anatomical trait data
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In this role you will develop cutting edge AI approaches to help researchers identify scientific information in historical manuscripts. Specifically, working with the Royal Horticultural Society
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the 1970-2010 average. The role will involve undertaking plant taphonomy experiments and generating chemical data from experimental and fossils plants to compare with morphological and anatomical traits and
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investigator and Career Consultants in the Careers and Employability Service on data interpretation, benchmarking and report writing. This includes gathering and systematic analysis of large-scale
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the link to our benefits website What next: Further information on the role is available on the linked job description. Short-listed candidates will be invited to interview and asked to deliver a
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, numerical modelling, data analysis, and interpretation of results. Alongside this, around 30% of the role will involve contributing to publications and actively supporting the development of future research
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obtained from Disclosure Scotland, or an overseas police check. Further information is available in the role profile. To apply for this vacancy please click ‘Apply Now’ to complete your details. It is a
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, Shin-Etsu. Applicants must hold a PhD (or close to completion) in a relevant plant or crop science field, have experience growing plants under axenic and/or glasshouse conditions, and possess strong data