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Field
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MR pulse sequences on Siemens platforms for quantitative imaging. Develop, adapt, and evaluate modern reconstruction algorithms for accelerated and robust qMRI. Collaborate with a multidisciplinary
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causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal
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) models with data-driven models (hybrid modeling) for improved accuracy and generalization. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive
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to distributed execution, checkpointing, filesystem performance, networking, and GPU utilisation Help maintain reproducible training recipes, configuration files, launch scripts, and documentation Work with
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Mission: Develop a monitoring and control system for an ammonia dehydrogenation plant. Functions to be developed: Perform plant modeling. Design an optimal control system. Design estimation algorithms
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. The specific distribution of effort will be adjusted to reflect the strengths and interests of the successful candidate but will remain predominately focused on research. Performance Objectives Conduct
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on proven results that estimate the average fragment size from machine vibration signals, the central research challenge is to recover the full distribution of fragment sizes — a substantially richer
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for postdoctoral fellowships in computer science with emphasis on the area of formal methods, in connection with topics that include (but are not limited to) computational complexity, distributed systems, human
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research and development activities in the field of underwater robotics, specifically applied to the distributed acoustic monitoring of the marine environment. The project seeks to explore the use of subsea
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by applying existing and novel computational biology, bioinformatic, and machine learning algorithms to sequencing datasets and correlating them with multi-dimensional clinical datasets that contain