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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills
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reconfigurable RF hardware for CAP-MIMO systems and contributing to machine learning-enhanced ISAC methods development through EM-informed modelling and hardware design. This is a unique opportunity to build
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, enthusiastic and willing to learn new skills. You will have highly effective verbal and written communication skills with all level of staff and an ability to operate effectively in a demanding and
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. The project integrates synthetic organic chemistry, kinetic analysis, automation, and machine learning to establish next-generation mechanistic workflows for asymmetric organocatalysis. The project advances
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, and machine learning. The environment at GBI will allow researchers to undertake ambitious, long-term, collaborative research, and we will actively support the translation of research to commercial
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, accumulated deformations, and their impact on structural performance, particularly for compression members. Develop data-driven reusability assessment platforms integrating NDT data, machine learning models