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skills: Good knowledge of ML/AI based techniques to develop fast surrogates (deep neural networks) and capability to develop own efficient model learning schemes (deep learning techniques, representation
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humanities team to engage actively in the development of the “Mapping Sonic Ecumene: A Visual Archive of Sound”. This involves mapping and archiving sonic and visual representations of songs across South India
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to benefit from local knowledge and prevent further escalation of conflict. For more details on the project, please see here . About the position: The Postdoctoral Researcher will have the following tasks
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framework for AI in gynecological oncology. We integrate symbolic knowledge representation (Ontologies/Knowledge Graphs) with Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to create
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entities, such as robots, vehicles, or sensors, forms internal representations of space, time, and motion when interacting in complex non-stationary environments. The objective is to study and develop models
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., drag schemes, mesh resolution, feature representation); document improvements and limitations. Collaboration, mentoring, and dissemination. Collaborate with DOE E3SM, ICoM, and InteRFACE, projects as
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with statistical evaluation methods. • Familiarity with AI technologies. • Knowledge of packaging performance testing standards. • Experience with fiber material properties, orthotropic material modeling
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-learning IT skills Excellent knowledge of English (C1) / Team player and high social/communicative skills What we offer: Work-life balance: Our employees enjoy flexible working hours, remote/hybrid and/or
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knowledge representation • Agent-based and simulation modeling • AI/ML, foundation models, causal inference, and predictive analytics • Human factors, behavior science, and patient-centered design • Advanced
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
, foster a stimulating research environment and advance knowledge within their fields. Postdocs are crucial members of our scientific research workforce, contributors to our research outputs and an important