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Field
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ideal for a researcher with a passion for solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights
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, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and
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platforms, including robotic systems, laboratory automation, and AI/machine learning-assisted approaches for experimental design, optimization, and materials discovery. The research will target advanced
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for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite
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electrochemical and CO2 removal research. Electrochemical process on interface phenomena MOF synthesis, testing under different conditions Simulation of scaled up process. Interface with machine learning group
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, and openly release evaluation code. What is Required: A recent Ph.D. (within the last 1-2 years) in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related
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; cost, carbon or sustainability optimisation; uncertainty and sensitivity analysis; and machine learning or AI-supported optimisation. Strong analytical and programming skills are essential. Relevant
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particular emphasis on integrating satellite LiDAR and UAV data with field observations. Applying statistical modelling, automated machine learning approaches, and artificial intelligence for the analysis and
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Data Analytics, including but not limited to: “Data Analytics for Social Research”, “Applied Analytical Statistics for Social Scientists”, “Machine Learning with Social Data” and “Social Networks
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intelligence, available at the Department of Informatics, in the Scientific Computing and Machine Learning (SCML) research group. Starting date as soon as possible/by agreement. The fellowship period is three (3