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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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this application process. 1. Go to http://www.umjobs.org 2. Click on "Login" (upper right corner). Use your umich uniqname and password. 3. Click on "U-M Graduate Student on the Ann Arbor campus
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on experience and qualifications). More information about this position is available on https://uol.de/en/job/research-associate-foundation-models-for-hearing-aid-processing-819 The position is embedded within
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are looking for a postdoctoral researcher in the field of image generation algorithms (specializing in Deep Learning) for a 12-month position. The work will take place within the IMAGE research team
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Algebra, Programming, Data Structures and Algorithms) and AI-related courses (Deep Learning, Feature Engineering). Candidates with experience in university teaching or R&D in related industries is an
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 3 hours ago
of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 3 hours ago
retrieval algorithm development with focus on using the polarimetric signals, the new FIR or sub-mm bands, and/or the ML/AI approach; (3) ML/AI application on system/pattern tracking on satellite images
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4 Oct 2025 Job Information Organisation/Company CNRS Department Laboratoire d'informatique de modélisation et d'optimisation des systèmes Research Field Computer science Mathematics » Algorithms
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences