478 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" PhD scholarships in United Kingdom
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is offering multiple studentships for candidates from backgrounds spanning the physical and computer sciences. These students will develop core expertise in robotic, digital, chemical and physical
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. Project proposals should assume utilisation of data from Crowdcube, Seedrs, and firm-level sources such as Companies House and Beauhurst, and consider use of natural language processing, computer vision
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for this research studentship, please complete the online application form. For further information contact us today: T: +44 (0)1234 758540 E: study@cranfield.ac.uk
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, the student will be able to design and execute reactions, interpret complex analytical datasets, and summarise this information in a variety of formats (such as in presentations at group meetings
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imaging. Novel data workflows will be developed to allow parallel multi-element measurements. The project is interdisciplinary, combining instrument development, analytical chemistry, laser physics and
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MEng degree in Electrical and Electronics Engineering or Aerospace Engineering. To apply or for further information, please contact Dr Sharmila Sumsurooah Sharmila.Sumsurooah@nottingham.ac.uk Funding
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PhD Studentship: Subcortical brain development and disorders, funded PhD at the University of Exeter
types in the right place at the right time to establish complex connections and functions. The dorsal midbrain, a subcortical brain region, is known to orchestrate the integration of sensory information
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motivated candidate with a 1st or high 2:1 degree in mechanical engineering, materials science, or a related field. Interest in advanced manufacturing and sustainability is essential. Experimental and data
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, please complete the steps below. PDF versions are available here to help with planning but submissions must be made using the forms above, emailed PDFs will not be accepted. Information Session There will
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knowledge for sustainable formulation design. Using different data-driven feature representations, AI foundation models will generate chemical embeddings to predict key physicochemical properties. Coupled