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intelligence and machine learning. The candidate will investigate how to formally define, measure and reconcile different, at times conflicting, notions of fairness in AI systems, as well as the theoretical
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and spatial modelling to assess willow expansion in different parts of Norway from recent decades to the present. The project will subsequently investigate the environmental and land-use factors
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listed in the project description above (ideally SAR for cryosphere applications) Strong programming skills (preferably Python or MATLAB) Strong background in statistics Fulfillment of the requirements
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synergistically integrates different renewable energy sources such as solar photovoltaic generation, hydrogen production, and storage alternatives with microgrid-driven power distribution. Through advanced
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project confronts the global imperative for adaptable, low‐carbon energy infrastructure by investigating a hybrid system that synergistically integrates different renewable energy sources such as solar
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to formally define, measure and reconcile different, at times conflicting, notions of fairness in AI systems, as well as the theoretical relationships between them. Possible directions for the project include
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metrology, profilometry or microscopy, together with statistical analysis in R, Python or equivalent software. Dental crown materials, restorative dentistry, prosthodontics or dental laboratory workflows
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and simulation using tools such as MATLAB/Simulink, DIgSILENT PowerFactory, OpenDSS or equivalent programming in Python, MATLAB or similar languages optimisation, control theory, machine learning
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, digital volume correlation, image registration, segmentation or quantitative image analysis. Surface metrology, profilometry or microscopy, together with statistical analysis in R, Python or equivalent
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inference Familiarity with administrative register data or other types of big data Familiarity with R, Stata, Python, or other relevant computing languages Documented research experience or educational