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Design and Synthesis of Next-Generation High-Performance Energetic Materials
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High-Performance Laser Powder Bed Fusion of Polymer Parts Using Quantum Cascade Laser Arrays
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Application Deadline: 01 June 2026 Details Project Overview We are seeking a highly motivated PhD candidate to undertake cutting-edge research in the field of Additive Manufacturing (AM) of high-performance
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performance, BCI deficiency and countermeasures - Teaching the BCI skill: feedback and human training approaches - Brain/neuronal computer games interfaces and interaction - Using BCI for monitoring and
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high-level training at the intersection of materials science, computational mechanics, and data-driven modelling, preparing graduates for careers in nuclear engineering, advanced energy systems, and high
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executing the foundational FAST-TRACK cycle. This involves manufacturing a diverse materials library, performing high-throughput characterisation, and conducting rapid manufacturing assessments
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accepted all year round Details The advent of easily accessible high performance computers or computer clusters and numerical techniques such as finite element methods (FEM) facilitates the highly accurate
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High-Performance Laser Powder Bed Fusion of Polymer Parts Using Quantum Cascade Laser Arrays School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Kristian Groom
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. You will benefit from: Access to high-performance computing resources: HPC Stanedge. A comprehensive suite of commercial and open-source software (e.g., Abaqus, Ansys, COMSOL, MATLAB, PyTorch, …). A
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round Details This project aims to develop a non-invasive brain-machine interface (BMI) that allows a user to direct a semi-autonomous robot to perform different tasks through brain signals