213 algorithm-development-"UCL" Fellowship positions at Nanyang Technological University
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devices including LEDs, photodetectors, solar cells, transistors, and single-photon diodes (SPDs) using cleanroom-based nanofabrication techniques (thin-film deposition, etching, etc.). Develop and
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for developing the microscopic models for magnon excitations for various experimental systems Numerical and analytic studies of the magnon thermal Hall effect for experimental detection. Requirements A PhD degree
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. Design, develop, and evaluate interactive systems or interventions that promote positive socio-emotional outcomes. Publish and present research in top-tier HCI and communication journals and conferences
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of the assigned research project. Additional duties may include training of and collaboration with research staff and students. Job responsibilities will include but not limited to: Collect and prepare data
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Materials, Bioinspired Materials and Sustainable Materials. The successful candidate will have the opportunity to develop chiral metamaterials, conduct atomic-scale characterizations of hierarchical self
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for highdensity topological information transfer. Key Responsibilities Demonstrate and measure the breakthrough concepts in nanophotonics using optical sources such a transmission electron microscope Develop high
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research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in
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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support
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project: “Climate Transformation Program (CTP): Cross Cutting Theme 1 – Sustainable Societies” funded by the MOE Tier 3C Grant. CTP aims to develop, inspire, and accelerate knowledge-based solutions and
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Responsibilities: Conduct advanced research in secure multi-party computation with applications to privacy-preserving machine learning. Develop scalable multi-party computation frameworks to enhance existing