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2,900 work in administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate
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computer code. Preferred Qualifications PhD in ecology, evolution, or closely related field. Expertise in one or more of the following biodiversity groups: fish, birds, reptiles, amphibians, insects
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 1 hour ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). The climate system can be considered in terms of the net imbalance between absorbed shortwave radiation
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/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in Quantum Information or Quantum Computing at the time of enrollment, and have experience and skills in algorithm design and implementation
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! This job offer is closely linked to a related opening at IET-3 at Forschungszentrum Jülich within the same project consortium: https://www.fz-juelich.de/en/careers/jobs/2025-362 Please feel free to apply
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closely linked to a related opening at IMD-1 at Forschungszentrum Jülich within the same project consortium: https://www.fz-juelich.de/en/careers/jobs/2025-361 Please feel free to apply for both available
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, tremor-signal acquisition, and algorithm development. 2. Characterisation analysis and interpretation of tremor data, including maintenance of datasets, documentation, drafting technical reports and
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available at https://www.fz-juelich.de/en/careers/phd We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic
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4 Dec 2025 Job Information Organisation/Company Grenoble INP - Institute of Engineering Department Engineering Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI