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Field
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. The aim of the project is to replace existing methodologies involving extensive experimental campaigns, which are generally expensive and time-consuming, even when supported by statistical design of
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of interventions (e.g., a campaign to promote physical activity or encourage recycling) by evaluating whether behaviour or outcomes following the intervention are statistically different from before the intervention
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the Office for National Statistics, and works in close partnership with a large network of partners and leading international research teams . We believe that a diverse and supportive environment
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/profile/yu-tzuwu.html ) Eligibility Criteria Applicants should possess a minimum 2:1 degree in epidemiology, statistics, public health, human geography, psychology or a related field that includes a
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to applicants with experience in one or more of the following: machine listening, deep learning, acoustics, signal processing, computer vision, spatial audio, NLP, statistical analysis, software development
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. Interdisciplinary focus This PhD project will take a critical ecological approach to assess how conservation decision making influences social outcomes. Supported by supervisors specialising in statistical ecology
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subject, such as psychology, biology, human genetics ii) an MSc in a relevant subject involving human genetic research or child development research iii) Prior experience working with statistical packages
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, publishing journal articles, conference presentations) Experience of using statistical and/or analytical software packages (e.g., SPSS, R, Tableau, Power BI, etc). How to apply Interested applicants should
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in access/ outcomes of mental health care pathways in the UK. Prospective candidates should be conversant with quantitative statistical methods and be committed to tackling health inequalities. PhD
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and progamming skills; Knowledge of Python.; Knowledge of Bayesian statistics, machine learning, and optimisation, or a willingness to learn. This scholarship is open to candidates of any nationality