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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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motion planning - Predictive collaboration with heterogeneous teaming - Multi-agent navigation in constrained environments - Data-driven and learning based control - Meta-learning for emergent behavior in
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for publications in journals and conferences collaborate with industry partners and leaders in the field of FPGA-based machine learning accelerators About you The University values courage and creativity; openness
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly
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for preprocessing, integration, and modeling of heterogeneous data (spatial, temporal, tabular) -Conduct research in explainable AI and uncertainty quantification applied to agronomic decisions. -Collaborate with
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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Claire O’Callaghan, the successful candidate will join a collaborative, interdisciplinary team, where they will be supported to develop their own research questions and lead projects. Experience in
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/agentic-AI frameworks leveraging reinforcement learning for complex clinical-reasoning tasks. In addition, Postdoctoral Fellows will: Collaborate with clinicians and researchers at the Montreal Neurological
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles