51 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Fraunhofer Gesellschaft" PhD positions at University of Nottingham
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for candidates who can demonstrate strong research potential. Suitable backgrounds include, but are not limited to: Human Factors Law (particularly law and technology, medical law, or data governance) Psychology
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Studentship Information Supervisor: Vinay Shukla Subject Area: Plant & Crop Science Research Title: Root oxygen dynamics and development Research Description: The student will be part of a
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differing aspect of this topic and could be reliant on both the interest of the student and supervisory team, plus what is found through the process of the research. Further information: Applicants should
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for fuel system applications. While these methods provide a wealth of knowledge and information, they remain impractical for industrial use. Therefore, AI modelling techniques will be harnessed to develop
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data-driven methods to develop an inverse design framework for manufacturing systems. Together, we will advance the capability to design manufacturing systems that embed reliability, resilience
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investigation) and basic quantitative methods (e.g., monitoring) Ability to collaborate and engage to different stakeholders Strong analytical skills and ability to handle data confidently Funding support After a
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Closing date: 27 February 2026 For further information please email Professor Chris Gerada (University of Nottingham) and Dan Walton (MTC). Facilities The MTC is an independent Research and Technology
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global manufacturers. For details, visit the MTC website . For further information on this PhD position please contact Dr Sara Wang (Sara.Wang@nottingham.ac.uk ) Closing Date: 27th February 2026. Proposed
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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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theory, robust and optimal control, and physics-informed modelling, this research aims to bridge the gap between data-driven learning and dependable real-world autonomy. Aim You will have the opportunity