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support needed to execute machine learning (ML) aims, handling large, complex datasets that exceed the capacity of general staff. By integrating daily data cleaning with advanced modeling, this position
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Deployment Strategies - Model Compression: Investigate techniques such as quantization, pruning, and knowledge distillation to reduce the computational and memory footprint of deep learning models without
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machine learning. The project aims to develop AI methods for mesoscale structural biology, understanding how cellular macromolecules organize into higher-order structures. You will work in a team at Janelia
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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expertise. The analyst provides support with respect to financial analysis and planning including modeling, research, forecasts, reporting, and the review of financial trends and budget and activity drivers
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letter with your application. Describe a deep learning project you have executed, ideally involving 3D image analysis, inverse problems, or physics-informed modeling. Cryo-EM/ET and computational
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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful
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machine learning for cybersecurity, current systems remain largely based on pattern recognition and struggle to incorporate contextual reasoning, temporal dependencies, and relationships between entities
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mathematical modelling Machine learning and/or other quantitative modelling in AI Statistical modelling Numerical analysis and scientific computing. The posts are full-time and are fixed term for a period of up
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 11 hours ago
experience applying data science, statistical modelling, machine learning, or bioinformatics methods within research or applied settings, ideally alongside the development of reliable and well-documented