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to learn laboratory methods for analysis of relevant BGC parameters. Training: You will be based in the Polar Oceans Team at British Antarctic Survey, a highly active research team focused on both
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spectroscopic methods suitable for large-scale sample screening and eventual field deployment. The project will also involve developing your skills in data science, including multivariate analysis, machine
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flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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machine learning with the logical reasoning and semantic understanding of symbolic AI (often referred to as material and design informatics) is being developed for the accelerated discovery and development
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trustworthy, we facilitate large-scale and reliable use of AI across different industries. Your work assignments You will work at the intersection of machine learning, cybersecurity, and privacy, developing
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expertise in research methodology or willingness to learn. Well-developed computer skills. Application process Expressions of interest are invited to be submitted electronically to Professor Judith Finn via
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in