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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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. The role involves contributing to this research project with a focus on model development, implementation, and testing. Further tasks involve dataset curation, analyzing results, and the creation
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communications signals. The objective of QUESTING is to develop new methods for quantum networking, fault-tolerant design and resource-efficient hybrid systems by training new generations of Q-System Innovators
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17 Feb 2026 Job Information Organisation/Company University of Malta Department Office for Human Resources Management & Development Research Field Engineering » Other Researcher Profile Recognised
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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of malignancies, blood disorders, and experimental therapies. Job Summary The Data Science, Analyst will will work to maintain and deploy algorithms for accurate detection, segmentation, and classification of cells
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passionate about applying ML algorithms and developing AI applied research and innovation solutions using classic ML to novel transformer neural networks. We test and measure the real customer impact of each
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will be expected to develop a nationally recognized, externally funded research program that is collaborative, interdisciplinary, and self-sustaining. The program should address critical challenges