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Field
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control algorithms for whole system efficiency optimisation. Design and simulate power management circuits using tools like SPICE or MATLAB/Simulink. Prototype and test circuits with real energy harvesters
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leverage low-precision accelerators for scientific computing by using a number of tricks, known as "mixed-precision" algorithms. Developing such algorithms is far from trivial. We can look at computational
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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of the processing system online. Our approach will be to draw on a broad selection of tools including (deep) reinforcement learning, queuing networks, online algorithms and systems engineering. In addition, a large
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environment of the aQa (applied quantum algorithms) group, which is a team of faculty, postdoctoral researchers, and students across the Leiden Institute of Physics (LION), the Leiden Institute for Advanced
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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tasks: Computationally design and simulate neuromorphic hardware including novel materials, devices and circuits. Implement bio-inspired learning algorithms on said hardware. Collaborate with
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areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. You will be creating data-driven algorithms which can solve state estimation problems in fluid
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context