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Field
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areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and
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systematic literature review, study design, and analysis of environmental health and exposure data while gaining experience applying epidemiologic, exposure-assessment, and biostatistical methods relevant
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
knowledge in at least one of the following areas will be highly regarded: the technology of large language models, natural language and text processing, qualitative and quantitative methods for evaluating
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of novel computational/biostatistical/machine learning methods for the integration of multiple, diverse dataset and the synthesis of hypotheses around the molecular mechanisms that drive the co-occurring
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healthcare innovation analyse clinical, imaging and health-related datasets using appropriate quantitative methods contribute to the development, evaluation or application of artificial intelligence and
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characteristics Drive to learn new methods and applications Curiosity and creativity in finding problem-related solutions Present and discuss your research with other professionals Engage and contribute
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(Qiskit, Cirq, etc.) - Fault-tolerant topological quantum computation - Quantum error correction - Topological / neutral-atom quantum computer & simulator platforms [Numerical & Computational Methods
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, omics, physiological signals, clinical notes), and causal AI (causal inference, discovery, counterfactual reasoning). The successful candidate will collaborate with an interdisciplinary team of computer
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is
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well as resource limitations. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning