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This PhD asks a different question: instead of demanding more data, can we build language models that learn smarter from less? You will design AI architectures that adapt to the structure of a
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, including offshore wind, battery storage, HVDC systems and other converter-connected technologies. As a result, electromagnetic-transient (EMT) phenomena increasingly influence system stability and operation
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for data-efficient vision foundation models. Foundation models in computer vision currently rely on massive datasets and brute-force scaling. This leads to high data requirements, hidden biases, limited
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Filled No Desired Start Date 10/01/2026 Job Summary The Aging Well Lab in the Center for Vital Longevity at The University of Texas at Dallas, under the direction of Kendra Seaman, Ph.D., is currently
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interpreted within a THMC physical modeling framework, in which deformation controls the evolution of permeability, fluid pressure evolves via porous flow, and reactions (serpentinization) modify bulk and
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biological routes for producing more sustainable materials. However, current approaches often use enzymes or microorganisms as passive additives rather than as programmable components. The E.CoLith project
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leader is Associate Professor Soledad Gonzalo Cogno. About the project The successful candidate will contribute to the development of mathematical and computational models to enquire about the mechanisms
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model
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can upload participants' written memory responses and receive automated scores in return, without writing any code. The platform is designed to host a range of scoring models (both the models we develop