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other machine learning models. Generate and evaluate hydrologic hindcasts and forecasts to assess model fidelity, forecast reliability, and predictive skill across subseasonal to annual time scales
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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools
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FieldMathematicsYears of Research Experience1 - 4 Additional Information Eligibility criteria - Thesis in natural language processing with machine learning, - mastery of NLP and machine learning methods and tools
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through applied research programmes. Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software
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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
strong machine learning and numerical modelling background to add knowledge on the impact of geological heterogeneity and subsurface environments (e.g., depth, exhumation, temperature, pressure) to de-risk
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chemistry modelling techniques scientific machine learning high-performance computing molecular design, generative AI, database handling and analysis collaborative, project management, presentation and
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treatments for mental illness. To this end, we bridge computational models that target various levels of analysis, including the algorithms (e.g., reinforcement learning models) and their neural