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Field
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skills in Python (experience with optimization or scientific computing libraries is a plus); excellent communication skills in English. For this position your command of the English language is expected
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together with other energy assets, such as electrical boilers, within a Model Predictive Control (MPC) framework that optimally balances electricity and heat production. Within FLEX-SMR, this PhD focuses
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, particularly large language models or multi-modality models Knowledge in deep learning optimization, such pruning and quantization Strong academic writing skill Personal characteristics To complete a doctoral
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-fabricated pavilion using a team of aerial robots. HARPA is looking for two motivated PhD candidates who closely collaborate to enable this vision: PhD #1: Multi-Agent Planning and Control for Cable-Suspended
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-modality models Knowledge in deep learning optimization, such pruning and quantization Strong academic writing skill Personal characteristics To complete a doctoral degree (PhD), it is important that you are
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optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities
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includes system operation, continued development, and performance optimization. The successful candidate will collaborate closely with the quantum metrology group at the University of Bonn and the geodetic
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for autonomous optimization and design of materials, structures and processes Multimodal scientific AI – integrating language, images, sensor data and simulations Alignment for scientific AI – scalable oversight
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background: familiarity with dynamic optimization, cost‑benefit analysis or macroeconomic modelling is an advantage; Experience with programming (Python, R, MATLAB or similar); Ability to collaborate
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considered a major advantage. Mandatory requirement: the candidate must hold a PhD degree and have previous research experience. Analyzing and optimizing financial performance for sustainable circular business