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], which states that random neural networks can be pruned to approximate a large class of functions without changing the initial weights. We are also interested in Neural Combinatorial Optimization, where we
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combinatorial panning methods, including phage and mRNA display, to identify de novo peptides for promising biomarkers lacking a natural ligand or lead structure. We then optimize peptide ligands for affinity and
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hits using in vitro (cell lines, organoids) and in vivo (mouse xenograft/PDX models) systems. They will optimize protocols for NGS library preparation, CRISPR-Cas9 editing, and high-throughput molecular
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University Seattle campus. Course topics include High-performance Computing, VLSI Design, Advanced Machine, Learning Combinatorial Optimization and Statistical Inference. Responsibilities include preparation
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(PCOG) at SnT, headed by Dr. Grégoire Danoy. PCOG specializes in optimization techniques, with a focus on distributed computing and AI-driven solutions PCOG conducts research in parallel computing, search
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challenging. In principle, one molecule of a bidentate ligand can be replaced with two molecules of monodentate ligands, enabling the use of combinatorial mixtures, which has been shown to lead to highly
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of monodentate ligands, enabling the use of combinatorial mixtures, which has been shown to lead to highly selective methodologies. This approach greatly simplifies the synthetic accessibility of viable ligand
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network