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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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language processing), machine learning, or data science. Preferred Qualifications Expertise in Deep Learning and LLMs(Large Language Models): Knowledge in building applications and fine-tuning models like Llama 3
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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in radiotherapy with the goal of enabling fully adaptive radiotherapy. The work is based on deep learning, where models are trained on generated or clinical data. The project is carried out in
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for candidates with a strong computer science background, such as algorithms, machine learning and data science. Key Responsibilities Develop, implement, and evaluate machine learning and deep learning models
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models and transformer-based architectures to construct high-dimensional design spaces. These models are integrated with Deep Reinforcement Learning (DRL) for fine-tuning or end-to-end learning, enabling
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across Africa using satellite remote sensing, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3 , N2 O, CO2 , CH4 ) using satellite observations, atmospheric
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Cultural Studies, History, or related field Demonstrated expertise with large language models (fine-tuning, prompting, deployment) Strong Python programming with deep learning frameworks (PyTorch, TensorFlow
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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in Ithaca, NY with a focus on developing deep learning algorithms. Dr. Haiyuan Yu, Ph.D. is a Tisch University Professor of Computational Biology in the College of Agriculture and Life Sciences and a