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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental
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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we
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to the fundamentals and algorithms of spatially and time-multiplexed oscillator-network computing. Duties The PhD student will focus on the fundamentals and algorithms for spatially and time-multiplexed oscillator
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. Doctoral studies end with a thesis and a doctoral degree. More about being a doctoral student at LTH on lth.se. Subject description This project aims to develop novel algorithms for Neural Rendering
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knowledge of collaborative software tools, and experience with the implementation of data acquisition or analysis algorithms You have a good track record of published articles in peer reviewed journals
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details and cross-layer interactions with communication and hardware can further affect the actions available to the adversary. We seek to develop analysis and design algorithms that incorporate cross-layer
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to extract knowledge from data, modelling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but are not limited to models and algorithms for knowledge
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. Activities include ultrafast quantum physics, quantum technology with rare earth atoms, quantum states in nanosystems, quantum information theory, quantum spectroscopy, quantum algorithms for optimization
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includes implementing and testing machine learning algorithms on quantum control tasks such as state preparation and qubit reset. You will gain hands-on experience with machine learning techniques and their
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Doctoral student in development of nanowire devices for photonic neuromorphic computing (PA2026/472)
dynamic photonic sensor array with on-chip neuromorphic intelligence. This is enabled by significantly advancing the state of the art in optoelectronic algorithm-hardware co-design and multi-scale