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-time, large-scale data demands of IIoT and lack sustainability considerations, such as energy efficiency. This research proposes a sustainable, high-performance IDS that leverages digital twin technology
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Develop methods and workflows to construct robust co-regulation networks from large
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network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring the use of large language models to support neural network design and data preprocessing
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on the BMI website: go.fzj.de/bmi.tvoed.entgelt Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at www.fz-juelich.de/en/careers/phd We
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agreement can be found on the BMI website (pay scale table on page 69 and following of the PDF download): https://go.fzj.de/bmi.tvoed Further Information: hds-lee@fz-juelich.de We welcome applications from
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-juelich.de/en/careers/jobs/2025D-183 Requirements Additional Information Website for additional job details https://www.fz-juelich.de/en/careers/jobs/2025D-183 Work Location(s) Number of offers
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used under the Skilled Worker visa rules. For more information please visit: https://www.gov.uk/skilled-worker-visa. As a valued member of our team, you can expect: 1 A warm welcoming and engaging
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Website https://www.fz-juelich.de/en/careers/jobs/2026D-005 Requirements Additional Information Website for additional job details https://www.fz-juelich.de/en/careers/jobs/2026D-005 Work Location(s) Number
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Midlands Graduate School Doctoral Training Partnership | Loughborough, England | United Kingdom | 4 days ago
the wellbeing equity gap related to housing inequalities? Methodological approach The project suggests a mixed-methods design. Quantitatively, the student will work with large and complex datasets — including
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning