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on developing new generative modeling approaches, scalable training algorithms, and foundation model technologies. The role is suited for candidates with a strong machine learning background who are excited
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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research in AI/ML and first principles modeling. Strong preference will be given for candidates skilled in machine learning/data science that have materials engineering/science research skills. Please
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University of California, Los Angeles | Los Angeles, California | United States | about 15 hours ago
mechanisms associated with immune phenotypes, as well as development and application of machine-learning and statistical models for classification, prediction, feature selection, dimensionality reduction
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therapeutics and antimicrobial peptides. The Postdoctoral Researcher will apply computational modeling, structural analysis, and machine learning approaches to support mechanistic studies of cellular signaling