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team to develop integrated control algorithms for autonomous cubesatellite formation flying. The controllers that will be developed during this project will differ from previous work on this topic by
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Engineering or equivalent from a recognized University. Major in Power Engineering will be advantageous. Minimum 5 years of relevant experience in power electronics, drives, control algorithms, optimization
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are looking for a candidate who can actively contribute to: Technical support in deploying, configuring, operating, and maintaining the Quantum Key Distribution (QKD) networks across the NQSN nodes Management
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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tokamak geometry on turbulent regimes and power-load distribution in double-null configurations Build-up a comprehensive database based on these simulations and analyze data Job Requirements: PhD degree in
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to optimize fluid flow, heat transfer, and current distribution within the electrolyzer. • Model key parameters such as overpotential, gas diffusion, ion transport, and pressure drop to improve overall
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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing
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Responsibilities: Development of stochastic and analytical methods for nonlinear partial differential equations Implementation of relevant numerical experiments using deep learning algorithms Job Requirements: PhD
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University of Singapore is dedicated to the interdisciplinary study of humans and algorithms on the Internet, and its implications on the society of the future. This is an exciting opportunity to join us as a
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reconstruction, 3D modelling software (Autodesk Revit, Dynamo), including open-source frameworks and algorithms for structural and M&E BIM reconstruction. 3. Experience in Python programming, machine learning