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systems and swarm robotics. The project builds on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation
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RAP opportunity at National Institute of Standards and Technology NIST Computational Modeling of Progressive Structural Failure and Collapse Location Engineering Laboratory, Materials and
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RAP opportunity at National Institute of Standards and Technology NIST Advanced computational modeling techniques to enable fast screening of RNA biopharmaceutical products Location Material
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/video segmentation, object tracking, reinforcement learning. Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation
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RAP opportunity at National Institute of Standards and Technology NIST Computational Modeling of Fracture and Fatigue in Hydrogen Environments Location Material Measurement Laboratory, Applied
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16.08.2023, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at the Technical University of Munich invites applications for the position of a Research Assistant
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Undertake testing on new AI models and systems to ensure reliability and alignment with experimental goals. Liaise with other engineers and stakeholders to finalise AI models and systems for use in
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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to have the following skills and experience. We welcome applications from individuals with experience in: Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep
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Smart Computer Models for Nuclear Reactor Design School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof S He Application Deadline: Applications accepted all year