19 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"SciLifeLab" Fellowship positions at University of Nottingham
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly
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strategies. Experience with qualitative research collection and analysis techniques, including at least one of the following: conducting data collection interviews, workshops, and/or textual analysis, is
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each year. Please check role profile for further information. For informal inquiries, please contact David Bates - david.bates@nottingham.ac.uk . Please note that applications sent directly to this e
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and contribute to the achievement of specific research objectives. For more information, please refer to the role profile. Requests for secondment from internal candidates may be considered on the basis
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theoretical areas including statistical physics, condensed matter theory, computational physics, quantum information, and machine learning. We seek motivated, skilled and highly independent researchers
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infectious aerosols in buildings (ASHRAE Standard 241). The work involves analysis, simulation, and integration of real-world data into risk models that directly inform how buildings are designed and operated
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searches and meta-analyses of dose–response and exposure–response data to support model calibration and uncertainty analysis. This role offers a rare opportunity to bridge public health and building science
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Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm
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Intervention in Psychosis service (Nottinghamshire Healthcare NHS Foundation Trust) to facilitate patient recruitment (up to 20% of your time). Analyse data, prepare publications, and present findings