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Field
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. Knowledge on multiphase (gas-particle two phase system), thermal energy storage, and/or renewable hydrogen technologies. Familiar with application of machine learning and deep learning algorithms to fluid and
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· developong new genetic algorithms related to finite element (FE) modeling · presentation of results and their disseminatio The successful candidates must be enrolled in PhD School program
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modeling tools and HDL simulators to validate functionality. Collaborate closely with algorithm designers to co-optimize architecture. Publish results in high-impact journals and conferences. Qualifications
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discovery, and deep-learning algorithms • Neutron scattering and advanced characterisation techniques The successful candidate will work closely with other PhDs and postdocs involved in similar investigations
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absorption/fluorescence and scattering experiments at X-ray free electron lasers. Your focus will be to derive new algorithms for interpretation of the scattering data by introducing chemical force-fields via
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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new generation of perceptual foundation models by contributing advanced perceptual pre-training and fine-tuning algorithms. What you will do You will carry out research and development in the areas
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. An optimisation tool has been developed that uses a genetic algorithm to optimise the location of BGI taking surface water flood risk reduction and the cost of different interventions into consideration. This PhD
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-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to