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modelling, microgrid technologies, and simulation-based research. Key Responsibilities Participate in and manage the research project with the PI, Co-PI, and team to design a robust mobile DC microgrid
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project deliverables are met. Collaborate with the PI, Co-PIs and NTU team to design and develop a robust and scalable software system for autonomous agents navigating immersive 3D virtual environments (e.g
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
Control group (https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/nonlinear-systems-and-control ) in the School of Electrical Engineering at Aalto University explores synergies
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R260014 Posting Link https://www.ubjobs.buffalo.edu/postings/60709 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.7 Posting Detail Information
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paradigm. Witnesses will be able to adjust lighting, toggle disguise features, and control viewing angle during lineups, creating a memory-congruent identification environment. The project will examine
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, detection, tracking, control, etc.). The integration of on-board AI requires new engineering methods, which are still poorly supported by digital twins. SAACD System of Systems (SdS): this corresponds
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IF or CORE A*/A conference paper. Robust machine control assumes modeling of robot-environment interactions. An example may include an outdoor autonomous ground robot that needs to be aware of its
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capital budgets. Through strong governance, robust financial processes, and cutting-edge systems, we ensure every dollar is put to its best use, fueling research, education, and transformation. Why Join Us
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technologies. Excellent command of the English language is required; German language skills are not required. Where to apply Website https://uol.de/job889en Requirements Research FieldComputer scienceEducation
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information about the network: https://euraxess.ec.europa.eu/jobs/401249 1. Context and Challenges Title: Physics-Informed Neural Operators (PINO) for Ultra-Fast Tomography: Toward Fundamental and Generalizable