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Field
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Epidemiology of Malaria), led by Aimee Taylor. Using simulation-based inference (SBI) with deep learning, UniGEM aims to build a neural network to estimate epidemiological parameters of P. falciparum
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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures
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of collaborative watermarking to diffusion models and audio language models based on neural audio codecs. Studying whether watermark information can be embedded in long-term content, such as speech semantics
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Carilion (VTC). About the Lab: The lab integrates state-of-the-art neural recording technologies, complex cognitive tasks, and computational models to investigate the neural basis of flexible cognition. From
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needed for wet-bulb temperature retrieval -Co-locating satellite data with ground-based HadISD stations -Running neural networks and finding the architecture best suited to a case study on India
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by connectome-constrained artificial neural networks. Candidates from outside the field of neuroscience are encouraged to apply, but must be curious, persistent, and passionate to delve
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include coastal features such as seagrass meadows, salt marshes, and dunes, and their dynamic interactions with waves, currents, and sediment transport processes. Artificial Neural Networks for coastal
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. Key Responsibilities Research & Development: Integrate Physics-Informed Neural Networks or Reinforcement Learning to create realistic human movement and interactive social behaviors within XR
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determines observables of the replication program such as the Mean Replication Timing (MRT) and the Replication Fork Directionality (RFD) profiles. We proposed a strategy to train a neural network to infer
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization