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Performance Computing Clusters. Preferred Qualifications Experience in Machine Learning analysis of complex image data sets. Experience with spaceborne and ground-based astronomical imaging data sets and
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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; aboveground biomass; fuel-density indicators; and other ecological and hydrologic indicators. Apply appropriate GIS, image-processing, statistical, and machine-learning methods to develop spatially explicit
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related discipline. Strong research background in Large Language Models (LLMs), Green AI and efficient machine learning, LLM-based Agents, Distributed AI systems and scalable model serving, etc Strong
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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: investigation of ontology-based metadata models for the machine-readable description of model structures, workflows and parameter dependencies, as well as a multi-agent architecture (LLMs, Retrieval-Augmented
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Strong skills in simulation methodologies Excellent communication and interpersonal skills Desirable Criteria: Familiarity with machine learning algorithms and artificial intelligence as applied
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an advantage: plasma surface functionalization; electrode/electrolyte interfaces; battery degradation modelling; microstructure-resolved modelling; tomography or image-based electrode modelling; machine learning
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advanced machine learning models, including multimodal perception and foundation model based systems, within distributed data, training, and inference pipelines. Perform hands-on research engineering at
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and reproducibility across the team. Machine Learning Model Development Collaborate with subject expert and software engineering team to create scalable machine learning based models for fisheries