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design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
these engagements will be integrated with outputs from advanced AI models (e.g.,geospatial deep learning, graph neural networks, and explainable AI ) to refine the AMR risk maps and ensure local relevance. Required
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out a rigorous scientific study aimed at comparing the performance of deep learning models in detecting complex visual anomalies. Take charge of the entire study, define the evaluation criteria and
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machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to [email protected] For more information about working at
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. The practical element of your project will be based on, but not limited to, time series analysis, network analysis, Bayesian inference, Machine Learning, as well as computational simulation of mathematical models
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science, artificial intelligence, or a closely related field. A strong background in machine learning and deep learning. A good understanding of Transformer architectures and modern AI models. Good programming skills
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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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novel methodology for the analysis of single-cell (multi)-omics data by incorporating existing biological knowledge into machine learning models. You will join a collaborative and internationally-oriented
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, topographic data, land-cover data, and other environmental predictors. Designs and evaluates statistical and machine-learning approaches for modelling plant species distributions and assessing environmental
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of the following areas: software development, Artificial Intelligence/Machine Learning, data processing, development and testing of computational services, or semantic technologies. Very good