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NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through machine learning, computer vision, and materials science. The focus of this position is on
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
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and electrochemical devices, by employing advanced experimental and theoretical methods in an interdisciplinary approach bridging synthetic chemistry, condensed-matter physics, and materials science
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DEPARTMENT The Department of Electrical and Computer Engineering at UTEP (http://ece.utep.edu) offers Bachelor of Science (B.S.) and Master of Science (M.S.) degrees in Electrical Engineering and in Computer
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devices, by employing advanced experimental and theoretical methods in an interdisciplinary approach bridging synthetic chemistry, condensed-matter physics, and materials science. Our research encompasses
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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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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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Physical Sciences for Advanced Materials and Technologies (MPHS) Program duration: 4 years No. of scholarships: 5 Coordinator: Prof. Nicola Fusco Contact e-mail: mphs@ssmeridionale.it 8. Modeling and