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. While the chosen design has a significant effect on the most important aspects of users’ experience, the algorithms, and thus the supply decisions, are based on users’ preferences. This PhD position
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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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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optimization algorithms, you will design structures that deliberately harness modal couplings to exhibit tailored nonlinear behaviour, with direct applications in ultrasensitive resonant sensing. Together
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the second direction, you will explore the geometric design of nonlinear systems. Using nonlinear reduced order modelling (ROM) integrated with optimization algorithms, you will design structures
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algorithms) Proficient in English. For information please check the Graduate Schools Admission Requirements. Familiarity or interested in using machine learning is also desiered. TU Delft (Delft University
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. Experience working on inversion problems (e.g., MCMC type algorithms) Proficient in English. For information please check the Graduate Schools Admission Requirements. Familiarity or interested in using machine
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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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have more realistic simulations. Supply and demand levels are intimately intertwined. While the chosen design has a significant effect on the most important aspects of users’ experience, the algorithms
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create metadata protocols to ensure consistent, reliable input for AI models. Design machine learning workflows to interpret large datasets, efficiently select algorithms, train and validate models, and