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models (transformers, diffusion models, GANs, etc.) * Quantum machine learning or tensor networks or quantum algorithms * Theoretical or applied aspects of quantum information Website for additional job
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Postdocs in Generative Machine Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures
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models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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gene gain/loss events, horizontal gene transfer, and functional diversification within gene families. You will apply statistical models and machine learning algorithms to identify associations between
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to multi-task and work cooperatively with others. Writing and communication skills. Preferred Qualifications: Knowledge of computer models on watershed assessment and/or flood management. Knowledge
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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mitigate turbulence under diverse urban layouts and meteorological conditions. To achieve this, the project explores advanced machine learning approaches, including surrogate modeling and reinforcement
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and data structures for AI kernels; Scalable systems for machine learning (training, inference, edge); HW-SW co-design for computer vision on novel architectures; Desired skills Advanced knowledge in
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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can