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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
; University of Sussex | The City of Brighton and Hove, England | United Kingdom | about 1 month agoBlack and other ethnic minorities – who are under-represented in postgraduate research at our institution. For enquiries about the application process, please email Emma Chorley: lifesci-rec@sussex.ac.uk
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the PhD student in high-performance computing, computer programming, applied mathematics, fluid mechanics, mathematical modelling and data analysis for large datasets -of the order of 100 Terabytes
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in human-computer interaction and have a strong collaboration with other research groups in Aalto and the EU. You would also be able to travel for visits or exchanges with other world-class research
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offering one funded PhD position investigating how LLMs represent and process linguistic knowledge, drawing on tools from computer science, linguistics, and neuroscience. The aim is to build new
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new family of solid-state Additive Manufacturing technologies, such as Cold spray. The nature of the process utilising low heat input and severe plastic deformation, produces ultra-refined
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inequalities shape carers’ lives using qualitative and creative methods (e.g. Theatre of the Oppressed, or others). 2) To support the co-production process across our team and partner organisations (e.g. Black
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encourage applications from underrepresented groups in STEM. How to apply (note there are 2 elements to the process): Please apply though the official University process. Instructions for applying through
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period. Enquiries regarding the application process may be directed to Prof. Serena Best at smb51@cam.ac.uk or Prof.Ruth Cameron at rec11@cam.ac.uk Please quote reference LJ46434 on your application and
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performance. However, calculating defect formation energies and migration barriers using first-principles methods remains a major bottleneck in the materials discovery process. To address this, we will develop
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations