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Field
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• Possess a PhD in Travel behaviour, Management, Economics, Statistics, Psychology, or related disciplines. • Has good understanding of discrete choice experiments and model estimation
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of extracting behavioural insight from increasingly complex and high-dimensional datasets? Would you like to help shape the next generation of behavioural modelling methods with applications in transport, health
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In this role you will support the delivery of two exciting, EPSRC-funded research programmes at the University of Nottingham, working across AI, human-computer interaction, and creative technology
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systems that affect their lives and maintain agency over the data generated. About the role We are recruiting a Research Fellow in participatory methods to work within the Centre for Data Futures (CDF
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complex and high-dimensional datasets? Would you like to help shape the next generation of behavioural modelling methods with applications in transport, health and the environment? Understanding human
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be preferred. Requirements: PhD degree in Learning Sciences, Educational Technology, Human-Computer Interaction (HCI), Information Technology, AI, or a relevant field Demonstrated experience and
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 23 hours ago
estimating treatment effects in the presence of intercurrent events whose occurrence may itself carry prognostic information. Methods of interest could include survivor average causal effect (SACE) estimators
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surgery. However, the growing use of TAVI has also increased the need for accurate methods to assess valve disease, identify patients who will benefit from intervention, determine the optimal timing
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mixed-methods research studies related to AI literacy and human-AI interaction. Develop and test AI-enabled interventions and output design features using Generative AI and chatbot platforms. Conduct
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trials, multiple endpoints, adaptive designs, Bayesian design and analysis methods, estimands, meta-analyses, benefit-risk analyses, subgroup analyses, biosimilars, patient experience data, bioequivalence