641 cloud-computing-"https:" "https:" "https:" "https:" "https:" "https:" "St" "University of St" positions at University of Sheffield
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Advanced brain-computer interface School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications accepted all year round
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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Using Brain Computer Interface to Improve Cognitive Performance School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications
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reliably stratify patients based on their fracture risk. The position is funded as part of Horizon 2020 European Project (https://www.metastraproject.eu/ ) that aims to provide a combination of models
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will use genomic and epigenetic datasets to address evolutionary questions in one of the best-studied wild mammal populations anyway in the world - the Soay Sheep population of St Kilda, Scotland
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mechanisms for apicoplast DNA compaction. In this collaborative, inter-disciplinary project, we will use cryo-EM (Lahiri lab https://sites.google.com/sheffield.ac.uk/lahirilab/), NMR (Willamson lab https
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lab https://sites.google.com/sheffield.ac.uk/lahirilab/) with structural bioinformatics (Chaudhuri lab, https://www.sheffield.ac.uk/biosciences/people/academic-staff/roy-chaudhuri) and plant genetics
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skill via an intrinsic reward. Science Advances, 10(13), eadj3824. https://www.science.org/doi/full/10.1126/sciadv.adj3824 Durstewitz, D., Koppe*, G., & Toutounji*, H. (2016). Computational models as
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for more details: https://sites.google.com/view/swagatosanyal About the School and Research Group The School of Computer Science of the University of Sheffield is ranked 8th nationally for the quality
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were constructed. The results will be compared with those for a charged scalar field [2]. The second part of the project involves computing the renormalized expectation value of the stress-energy tensor