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Mechanical Engineering Technology Program with Engineering Design (Fusion 360), CAD/CAE (Solid Works), Engineering Computing (Matlab), Finite Element Analysis (Solid Works) skills preferred. A strong
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, disentanglement) Audio modeling Multimodal learning (alignment, data fusion) Statistical learning and advanced deep learning (large-scale training, generation control, self-supervised methods, and contrastive
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The University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 2 months ago
, providing tier 3 support as needed. This position develops staff capabilities through technical guidance and maintains centralized monitoring systems such as Crestron Fusion and Extron Global Viewer
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, passionate and have a deep interest in and strong desire to pursue a career in plasma physics, fusion science, computational science, engineering or a related field. This prestigious fellowship, which advances
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for a highly motivated and well-qualified individual to work on the ERC StG project »Melting-Process-Condition Variations-Aware Control of the Laser-Powder Bed Fusion of Metals« (MeltingWell), GA
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the setup of experimental facilities Basic understanding of heat transfer and structural mechanics; experience with FEM methods (e.g. ABAQUS, ANSYS) is an advantage High motivation to contribute to fusion
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of fusion energy, a potentially limitless energy source. PPPL is also using its expertise to advance research in the areas of microelectronics, quantum sensors and devices, and sustainability sciences
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 2 months ago
guidance and maintains centralized monitoring systems such as Crestron Fusion and Extron GlobalViewer for classroom AV equipment oversight and reporting. Summary of Position Responsibilities Designs
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performance, detect artefacts, and ensure scientific rigor Proficiency in Python (or Julia/R) for data handling, ML model development, and statistical analysis Ready to work with time‑series data, sensor fusion
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from data from a multimodal sensor. Merging data from multimodal sensors located in different spatial positions: calibration and synchronization. Transforming fused data: sensor fusion and feature fusion