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Models, Knowledge Graphs, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the following areas: Natural Language Processing, Knowledge Graphs, Machine
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on tungsten samples and candidate tungsten alloys will validate the simulations and guide the design of more dust-resistant materials. Finally, we will use machine learning to integrate simulation and
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optimization and modelling. Interest in topics such as integrated water management and treatment, multi-objective optimization strategies, game theory, and machine learning. Proficiency in English. Desirable
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reserves the right to maintain a professional atmosphere conducive to learning; therefore, if a faculty member deems a model’s personal hygiene to be below acceptable standards, the model will be dismissed
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network structures. Methods from graph theory, machine learning, and artificial intelligence will be employed to model complex relational structures and identify patterns in high-dimensional data. The work
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for ethical AI. Integrate machine learning models, visualization dashboards, and backend services. Contribute to data collection, testing, documentation, and dissemination of open-source resources
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Research Scientists as part of its new initiative, Polymathic AI, Building Foundation Models for Science. Recent advances in machine learning, including Large Language Models and diffusion based generative
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, Computer Science, or related field Preferred Experience: Experience with machine learning in medical imaging/biomechanics; grant writing support; clinical gait analysis in clinical/research setting; gross anatomy
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of experience in training, evaluating, and deploying machine learning models, including deep neural networks and relevant frameworks - Documented several years of experience in systems development with Python and
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understanding of deep learning concepts, various network architectures, and practical model development. Experience with model deployment, experimentation, or reproducible machine learning workflows. Experience