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techniques (e.g. explainable, ethical, empathic and agentic AI), natural language processing (NLP), large language models (LLMs), data science methods, and mHealth to analyze large-scale, multidimensional, and
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large-scale data analysis Related fields such as nutrition, anthropology, or environmental science Project management experience, particularly in coordinating multi-site studies or citizen science
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experimentally verified results is a necessary: - Autonomy with collaborative intelligence - Distributed task and motion planning - Predictive collaboration with heterogeneous teaming - Multi-agent navigation in
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related field in hand by the time of the appointment. Strong background in distributed control, optimization, or multi-agent systems. Proven track record of high-quality publications. Proficiency in
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in ecological fieldwork, ideally in wet grassland or agricultural systems - Skills in GIS, remote sensing, and spatial data analysis (bonus: agent-based modelling) - Demonstrated ability to work in
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effectiveness of therapeutic agents and utility of biofluid biomarkers in pre/post-blast animal models, human volunteers and victims of blast exposure. It is fully anticipated that the multi-faceted experiences
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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and point-cloud streams are massive, wireless links are unreliable, and safety demands that information be both timely and trustworthy. IONIAN tackles this bottleneck head-on. We will re-invent multi
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sparse-regression based techniques to derive interpretable and computationally efficient differential equation models from computationally intensive multi-cellular agent based models (ABMs) of Epstein–Barr
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is concerned with the challenging problem of modeling the complex modern radio environment, where a diverse set of devices and agents share the available spectrum. In this environment, it is crucial