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Field
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learning, and computational biology to adapt and augment existing open models for predictive biology. Approaches may include fine-tuning, parameter-efficient tuning, probing, and retrieval- or knowledge
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AI literacy, critical evaluation capabilities, and workplace performance among knowledge workers. The project sits at the intersection of AI literacy, human-AI interaction, workplace learning, and the
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may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums, Collections and AI 3D Scanning and Digital Heritage Extended Reality (VR, AR and Mixed Reality
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Registrations Other Required Certificates, Licenses and Registrations Preferred Certificates, Licenses and Registrations Preferred Other Certificates, Licenses and Registrations Knowledge, Skills and Abilities
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hardware design using Verilog/SystemVerilog or equivalent HDLs. Sound knowledge of machine learning algorithms and their hardware implementation considerations. Demonstrated ability to conduct independent
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algorithms such as PINN, SKINN. Building agentic economic world models that are incorporated with theory and knowledge structures and connecting them with reinforcement learning and generative modelling
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intelligence algorithms using Python. Knowledge of machine learning or reinforcement learning techniques is highly advantageous. Experience with theoretical wireless network modelling, particularly stochastic
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learning or computer vision conferences/journals, such as TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, ICML and ICLR. Knowledge of scientific or engineering software like PyTorch, TensorFlow, Keras, etc. Research
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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top-tier machine learning or computer vision conferences/journals, such as TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, ICML and ICLR. Knowledge of scientific or engineering software like PyTorch, TensorFlow