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interfaces under reaction conditions, using machine-learned interatomic potentials (MLIPs) for automatic reaction network exploration for catalyst dynamics, and developing the next generation of kinetic Monte
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, output validation and reporting. Developing integrative strategies for a diverse set of data, integrating the outcomes to inform future projected trend analysis. Applying statistical and machine learning
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machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy. Research directions will be flexible and shaped according to mutual
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large, highly diverse and multi-modal datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet
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research. The successful applicant will possess a relevant PhD or equivalent qualification/experience in a relevant field of study (e.g. mathematics, physics, statistics data science, AI, machine learning
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-house experiment results Use state-of-the-art machine learning models to develop a multi-scale droplets evaporation model Assists in co-supervision of Final Year Projects (FYP) or capstone projects
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to learn. Interest in tumor metabolism and a strong desire to gain experience in bulk and spatial metabolomics. Solid bioinformatics skills, including familiarity with tools for analyzing omics datasets and
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cardiac precision medicine through artificial intelligence and machine learning. The postdoctoral fellow will contribute to the development of a comprehensive, multi-modal framework for predicting and
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of 17% superannuation applies. Research Fellow in machine-learning enabled digital forensics Fixed term, full-time 36-month position available About the position We are seeking a Research Fellow with
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning