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Job description: The University of Vienna is a cosmopolitan hub for more than 10,000 employees, of whom around 7,500 work in research and teaching. They want to do research and teach at a place that
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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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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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/Actualites/Intelligence-artificielle… The goal is to have several teams "coopeting" (collaborating and competing at the same time) to solve major data challenges in marine science. The challenge of interest
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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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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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Machine Learning models for quality prediction and control of phosphate ore. Simulating and evaluating various extraction scenarios, considering industrial constraints. Collaborating closely with field and
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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