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on monitoring and modeling research. • Compile and analyze current scientific data on GHG emissions in freshwater ecosystems, focusing on CO2 and CH4 emissions. • Acquire, organize, and evaluate GHG emissions
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tools, collaboration with project stakeholders, and engagement with the consortium and Defence and Security stakeholders. Technical Requirements: Strong coding skills with background in machine learning
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. Responsibilities include querying databases, data processing, supervised and unsupervised machine learning, deploying production models, and communication of scientific findings via peer-reviewed publications and
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/Artificial Intelligence. The UW Mechanical Engineering Department has achieved international prominence in the use of Machine Learning and Artificial Intelligence in the modeling and control of physical
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Professor Arbel’s lab in the context of a grant focused on developing multi-modal causal deep learning models to predict future disability progression and treatment response in Multiple Sclerosis
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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team, your expertise in AI and other cognitive computing methodologies, such as but not limited to machine learning (ML), large language models (LLMs), small language models (SLMs), natural
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publications in CAV/traffic modelling, computer vision, or reinforcement learning. Hands-on with YOLOv8 + DeepSORT and RL platforms (e.g., FLOW) for traffic control. Excellent English communication - essential
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power users to design and implement infrastructure that supports analytics and machine learning workflows, including feature engineering pipelines and model outputs integration. Use Python or R to support
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research and model development for Artificial Intelligence, especially multi-agent systems and NLP. Experience in prototypes and intelligent tools development. Evidence of developing Machine Learning and