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solutions to the key challenges of real-time condition monitoring. Analysis of large spatial and temporal remote-sensed datasets and development of advanced models to enable the monitoring of historical and
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machine learning model development; Experience designing research projects and managing project delivery; Experience processing, documenting, and analyzing data sets; Ability to excel in a highly
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, engineering, finance, and health. Key Responsibilities: To perform the pioneer research in AI for climate transformation. To further develop data-driven and machine learning tasks for fighting climate changes
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to treatment, population health monitoring, workforce development and leadership, policy, and advocacy. Background The Robotics, Autonomy and Machine Intelligence (RAMI) Group led by Prof Nabil Aouf is dedicated
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machine learning model development; Experience designing research projects and managing project delivery; Experience processing, documenting, and analyzing data sets; Ability to excel in a highly
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Yield Forecasting". This project aims to revolutionize agriculture in Morocco by combining cutting-edge technologies, including crop growth models, remote sensing data, data assimilation, machine learning
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, Bayesian and maximum likelihood approaches, spatial statistics and random forests or other machine-learning approaches and be quick to learn new techniques. Enjoyment of analysis of large and spatially
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the research projects - “Monitoring and predicting slope instability in forested terrain from multi-mode remote sensing data” and “Resilience of rural infrastructure and communities to climate change
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods