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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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contributes to improving large-scale plasma simulations that are essential for the design and optimization of nuclear fusion devices, a key step toward future sustainable energy technologies. You will conduct
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development. The successful candidate will contribute to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model
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biodegradation of biomass-derived materials, engineering microbiomes to optimize end-of-life material processing and circular resource recovery, as well as leveraging plant-microbe interactions to enhance material
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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engineering and biotechnology. A PhD position is available in the Advanced Production Engineering (APE) research group that deals with the development, optimization and implementation of advanced production
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
for realistic haptic control and feedback, and real-time agent-based simulation for guiding optimal work performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human
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, United States of America [map ] Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview As a Quantitative Systematic Trader at Susquehanna, you’ll combine
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for realistic haptic control and feedback, real-time agent-based simulation for guiding optimal work task performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human
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garments into recycle, reuse, and manual-review streams; this PhD project tackles the core challenge of designing and optimizing a high-throughput hyperspectral imaging system, fused with complementary