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Field
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML
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undertake comprehensive literature and market surveys, develop advanced simulation models, investigate integration into HVDC transmission systems, and design/test scaled-down hardware models at the ‘Wolfson
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for clinical use. Generative and Predictive AI for Clinical Decision Support and Statistical Inference Develop biologically informed statistical methods and uncertainty estimation models to train deep learning
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of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
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of cities. Develop the first national land use–transport interaction (LUTI) model for Australia. Evaluate policy scenarios involving HSR to realign population growth with sustainability goals. The selected
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can
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with host bacteria during infections, particularly focusing on the use of E. coli and its related phages as model organisms. However, the results may be applied in pathogenic bacteria related to E. coli