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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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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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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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digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different conditions. To propose a methodology/framework in a
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Responsibilities: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety
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progress. Ability and willingness to work some flexible hours. Extensive experience in large-scale pre-training of large language model. Experienced in developing machine learning algorithms and large
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Responsibilities: Conduct research in Systems Biology to study cell signaling Perform large-scale signaling analysis or protein-interactome analysis Utilize machine learning-based biological data mining and analysis
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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
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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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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative