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Field
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Associate to join the Artificial Intelligence (AI) & Machine Learning (ML) Lab, under the direction of Dr. Bo Liu. The team is a collaborative partnership between nine Universities across the US led by the
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via autophagy and lysosomal targeting in learning and memory and disease models using rodents and iPSC-derived cell cultures. These mechanisms will be investigated in both healthy conditions and
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of design, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and
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, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and computational
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of the field through the development and use of machine learning, deep learning, and high-performance computing (HPC). This position resides in the Chemical Separations Group in the Separations and Polymer
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in Python, Machine learning, and Uncertainty Quantification in engineering applications, Preferred: Background in space weather and astrodynamics Excellent written and oral communication skills
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research. This exciting position offers a unique opportunity to develop cutting-edge Knowledge Guided Machine Learning (KGML) methods for advancing our understanding of agroecosystem carbon and nitrogen
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acids, ligands), coarse-grain and polymer model development, multiscale modeling, molecular simulation code/software (e.g., LAMMPS, GROMACS), machine learning. Prior experience with applying simulations
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to work on a project at the intersection of deep learning and computer security/privacy, under the direction of Dr. Michael Wu. The project seeks to investigate security and privacy problems in deep
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experiments in the field of neuroscience, computational neuroscience and biologically plausible machine learning tools. Duties: Analyze data and developing biologically plausible machine learning frameworks