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, scientific computing, and emerging AI. The project tasks will include resource-aware training methods and hardware implementation, including the design, implementation, and verification of KAN accelerator
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University of Luxembourg Location Esch-sur-Alzette, Luxembourg Salary Negotiable Closing date 17 Oct 2026 View more categories View less categories Discipline Computing , Engineering Job Type PhD Position
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and applying optimisation methods, such as model predictive control, multi-objective optimisation and, where appropriate, reinforcement learning Investigating suitable model-reduction and approximation
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library for computing the elementary matrices associated with near, singular and quasi-singular interactions using advanced integration methods from the literature [2–6], or variants to be defined as
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include: Developing and analyzing representative dynamic simulation scenarios for electrical power systems Extending kernel-based Gaussian Process methods for the estimation of electrical and control
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requires an unprecedented computing and storage infrastructure. Processing these enormous amounts of data in a future data center, however, consumes significant resources. The fundamental challenge is: How
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and develop novel methods to improve their temporal representation learning and downstream regression performance. The goal is to design more flexible temporal modeling strategies and improved
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background in numerical methods, mathematical modeling, and network simulation or analysis Good understanding of power systems, gas networks, or coupled multi-energy systems Proficient in programming
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and sufficient. Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning. A parallel line of
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installations (laser fs or ns, XFEL, synchrotrons, high pulse power), and developing numerical simulations and analysis tools. In order to develop new models into ESTHER, the candidate will be interacting with