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fellows and other researchers. Strong interpersonal and written and verbal communication skills and ability to effectively train users and staff on proper procedures and methods for using advanced
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to the research project itself. The remaining six months will be allocated to formal training. As a PhD candidate, you must participate in the Faculty of Humanities’ educational programme . The PhD programme
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opportunities to build an international research portfolio. The post holder will work on developing and testing study design and data analysis methods, particularly related to cluster randomised trial designs and
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will be allocated to formal training. As a PhD candidate, you must participate in the Faculty of Humanities’ educational programme . The PhD programme comprises a training component corresponding to 30
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or public health research. Previous experience of applying statistical methods to epidemiological studies, including causal inference methodology, is desirable. You will possess excellent organisational
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living with dementia. The successful candidate will work across a variety of tasks and research methods to evaluate or develop and implement technology for longitudinal assessment in the home environment
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and/or quantitative methods. Is confident working with a range of stakeholders, including members of the public, emergency services staff, and industry collaborators. Can work independently and
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to process and often have to be postponed to a later Selection Committee meeting. The deadline for meeting certain formal conditions – in particular those relating to limitations on career stages and previous
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commensurate to the candidate's qualifications and experience. Informal enquiries are welcome and should be made to Ms Suhana Sulaiman, at suhana_s@nus.edu.sg Formal application: Please submit your application
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
of drug development, regulatory science, and advanced computational modeling. This position will involve integrating mechanistic modeling and machine learning methods to analyze and predict drug properties