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advance research in computer vision, machine learning, and/or robotics for the digitalization, monitoring, and automation of civil infrastructure. The role will focus on developing innovative methodologies
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frameworks for advanced property prediction and analysis of inorganic disordered materials. Carry out machine-learning based first-principle calculations aimed at advancing the understanding defect-based
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in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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The AI4X Postdoctoral Fellowship supports outstanding early-career researchers who leverage Artificial Intelligence (AI) to accelerate breakthroughs across science, technology, engineering, and
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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to enable high quality work in strategic directions that are of significant impact to industry, science and technology. We are looking for a Postdoctoral Research Fellow to advance research in developing
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including functional enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning