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Field
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dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable though not necessary
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expecting to obtain, at least a 2:1 honours degree or MSc with Merit (or international equivalent) in a relevant subject. Potential PhD Project Areas Plant reproduction • Cell signalling during plant
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sensitive to malicious deviations while remaining resource efficient. Solutions must operate effectively on network gateways or even capable IoT devices. The research will investigate statistical methods
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required, along with an eagerness to learn more detailed identification. Any specialist knowledge of ground-dwelling invertebrate taxa is a bonus. Support: The student will be based in the Ecology and
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
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participants from diverse cultural backgrounds (E) Data analysis skills, including the use of quantitative statistical techniques (E) Experience in the interpretation of research data and results (E) Ability
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and manufacturing methods. The Centre's contributions to industry are demonstrated through its extensive MSc and PhD research initiatives and its ongoing technology development programs in large-scale
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commercialisation efforts. Peer reviewed publications in medicine and psychology journals. Eligibility: Applicants should hold or expect to gain a minimum of an MSc in Psychology, Public Health, Epidemiology
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are demonstrated through its extensive MSc and PhD research initiatives and its ongoing technology development programs in large-scale additive manufacturing. This project will be closely aligned with the ATI