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- bermet.ishenbekova@ucentralasia.org
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Field
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synthesis by self-assembly and mechanistic analysis. The successful candidate will have a PhD in either inorganic or materials chemistry with some experience of analytics for mechanistic analysis
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modelling and eigenmode analysis for photonic crystals; terahertz far-field spectra measurements or near-field scanning spectroscopy. In this role, you will join an interdisciplinary research group
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Data analysis and interpretation Apply knowledge in a way which develops new intellectual understanding Disseminate research findings for publication, research seminars etc Contribute to developing new
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systems in affected regions. In this role, you will contribute to the development of the study protocol and regulatory submissions, support trial implementation, and oversee the management and analysis
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research interests compatible with those of our current faculty in the following research groups: mathematical biology, mathematical and computational finance, numerical analysis, machine learning and data
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to learn new techniques. Experience with data processing, analysis and interpretation. Ability to keep up to date with new developments in relevant research fields. Ability to work both independently and as
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. Supported Areas for START - Business, Communication and Design Financial and Sustainability Accounting: The measurement, reporting, and analysis of an organisation's financial performance alongside its
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assessment, malware analysis, privacy technologies, and AI security AI and Emerging Fields: Smart healthcare, IIoT, computational fintech, robot-human interaction, trustworthy AI, explainable AI, large
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participant recruitment, data collection, keeping meticulous records of participant retention, working with our PPI and governance representatives, data analysis, writing for publication and event planning
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the South Asian diaspora. Duties will include participant recruitment, data collection, keeping meticulous records of participant retention, working with our PPI and governance representatives, data analysis