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Field
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. Applicants are strongly advised to contact Professor Newey for an informal discussion about research proposals. For more information about the project and informal enquiries, please contact: Primary Supervisor
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, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners. For further information, please contact sharmila.sumsurooah
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-time network management and robot coordination. The project will address key factors such as seamless connectivity, low latency, and enhanced reliability, accounting for heterogeneous mobility, data
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exploration to enable efficient mapping of unknown environments. Emphasis will be placed on leveraging SatCom connectivity and heterogeneous sensor data and real-time decision-making to adapt to complex
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information in the ‘Further Details’ section: a ‘Personal Statement’ (this is a mandatory field) – Use this template . the studentship code DLA2511 in the ‘Studentship/Partnership Reference’ field. when
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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modeling and data analysis. An interest in groundwater contamination, risk assessment, and sustainability. Programming experience (Python, MATLAB, or similar) is desirable but not essential. However
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requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website . Funding information: The studentship is for 3 years, full time
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and application of statistics for data analysis. Experience of using qualitative and quantitative research methods will be vital. To apply, please send a CV, a cover letter summarising your research
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cohort to benefit from peer-to-peer learning and transferable skills development. For full information about the programme, the research projects available and how to apply, please visit: http