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University of Sheffield Theoretical Physics PhD Projects, Programs & Scholarships

We have 5 University of Sheffield Theoretical Physics PhD Projects, Programs & Scholarships

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  EPSRC-SFI Centre for Doctoral Training in Advanced Metallic Systems – Call for Applications for 4-year Funded PhD and EngD Studentships

Funding Type

PhD Type

We are looking for one hundred STEM graduates excited to improve the world via impactful research in new and novel metallic materials for power generation, transport, sustainability and recyclables, in a rapidly evolving digital manufacturing era using advanced computing techniques.
  PhD on Waves in Complex Materials
  Dr A Gower
Application Deadline: 31 July 2019

Funding Type

PhD Type

A three and a half year fully-funded PhD post is available to work on the physics of waves (sound or light) in complex solids and fluids under the supervision of Dr Art Gower (https://arturgower.github.io/).
  Biophysical modelling of bacterial cell wall architectures
  Dr B Chakrabarti
Applications accepted all year round

Funding Type

PhD Type

Bacterial cell walls are examples of self-assembled composite structures that are formed out of equilibrium. In recent times the text book view of bacterial cell wall has been modified as a result of high resolution imaging studies[1, 2].
  Mesoscopic modelling of Polymer Nano-composites
  Dr A Parnell, Dr B Chakrabarti
Applications accepted all year round

Funding Type

PhD Type

The statistical theory of transport in polymer networks and gels, which are examples of mesoscale porous structures holds many challenges, including connections between structures across length scales, their associated mechanics, and role of thermal fluctuations.
  Self-organisation as a theory of brain development
  Dr SP Wilson, Dr JV Stone
Applications accepted all year round

Funding Type

PhD Type

Self-organisation is a general theory of how function is assigned to cortical microcircuits. The theory predicts that a balance between cooperative and competitive interactions in local cortical circuits, consolidated by Hebbian learning, results in similar inputs being represented by nearby neurons.
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