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work environment Learn more about CQT at https://www.cqt.sg/ Roles & Responsibilities Administrate CQT PhD students candidature milestones (eg. QEI, QE II, Progress Report, Oral Defence etc) Administrate
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. The practical element of your project will be based on, but not limited to, time series analysis, network analysis, Bayesian inference, Machine Learning, as well as computational simulation of mathematical models
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concepts into accessible, domain-relevant learning experiences for students. The full job description is available here: https://computing.mit.edu/lecturer/ Job Requirements REQUIRED: PhD in Computer Science
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 10 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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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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, and machine-learning-based analytics. The research work at NTNU will focus particularly on automation, robotics, mechatronic design, sensor integration, and intelligent experimental systems required
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, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results into useful decision support Publish and communicate results and
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decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch energy
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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration, experimental testing, or hardware-in-the-loop