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Field
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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/or mixed-methods; no parallel enrollment in another PhD programme; fluent English language skills are a must; knowledge of Bulgarian is an asset; no previous contractual links to the IPS at any point
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant REMODEL: Advancing Parallel Mesh Generation and Geometry Representation to Enable Industrially Relevant
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. CPUs/GPUs) and their use in high-performance computing through shared or distributed parallel programming (e.g. OpenMP, MPI). Strong programming ability in C++ or a related language. Experience in
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architectures (e.g. CPUs/GPUs) and their use in high-performance computing through shared or distributed parallel programming (e.g. OpenMP, MPI). 3. Strong programming ability in C++ or a related language. 4
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: 280599364 Position: Postdoctoral Research Associate Description: The Computational Turbulent Reacting Flow Laboratory, led by Prof. Michael E. Mueller, invites applications for postdoctoral or more senior
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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
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on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and
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input from a parallel international project (in collaboration with Prof. Joni Lakin, Dr. Jonathan Wai, Prof. David Uttal) and industry collaboration via the partnership with GL Assessment (Dr Berny