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Field
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geophysical investigation, geomechanical engineering, and machine learning. The Centre for Urban Solutions is to provide leadership in developing innovative solutions and sustainable technologies for space
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approaches that support evidence-based decision-making. This may include optimising transport/manufacturing operations, alternative fuel/ technology pathways, infrastructure capacity, fleet/machine composition
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Job Description Job Alerts Link Apply now Research Assistant (Computational Science and Machine Learning) University-Level Unit: College of Design and Engineering Faculty/Department-Level Unit
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Research Associate / Magma Research Fellows who have: a PhD in mathematics, computer science, or a related discipline a strong background in computational mathematics, computer algebra, or mathematical
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, or near completion, of a PhD in Electrical Engineering or a closely related discipline. Demonstrated expertise in power system dynamics, stability analysis, renewable energy integration, or related areas
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-science and signal-analysis tasks, including processing experimental signals, integrating datasets, developing machine-learning models, and mapping measured fuel properties to SAF performance. For the post
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-making. This may include optimising transport/manufacturing operations, alternative fuel/ technology pathways, infrastructure capacity, fleet/machine composition, resource allocation, scheduling
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or connectivity analysis; machine-learning or deep-learning methods for geospatial analyses; ecological or remote-sensing fieldwork, particularly in alpine environments; Google Earth Engine, geodatabases or cloud
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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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. Further funding to support the post may be available. About you Applicants should have a PhD (awarded or imminent) in Electronic/Electrical Engineering, Biomedical Engineering or a closely related