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machine learning, particularly focusing on computing with Oscillatory Neural Networks (ONNs). Project The project aims to formalize the synergies between physics, information theory, and machine learning
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the development of foundation models that have expanded the boundaries of machine capabilities. However, learning these models requires direct access to vast data repositories, which poses significant privacy and
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reasoning to define and analyze system components and their interactions. Merging machine learning with symbolic AI to enhance system (performance) monitoring. Advancing adaptive surrogate modeling
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of computational neuroimaging, where you'll experiment with the latest advancements in computer graphics, leveraging techniques from the filmmaking industry to create visually compelling representations of complex
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-based and computer vision-based automotive perception, and domain invariance/resilience. Designing methods for domain invariant deep representations of radar-specific embeddings. Developing learning
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tools). Experience with hardware architecture, FPGA, and system-level simulation is desirable. A good theoretical understanding of statistics and machine learning theory. Strong analytical skills and
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interfacial properties, colloidal interactions) and computer simulations. Of particular interest are designing molecules, particles, and interfaces to perform a specific function, and to develop novel
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for this development, but will work in a team of other PhD candidates, postdocs and technicians that will assist. Finally, the full 0.7-MV prototype will be developed along similar lines and with all lessons learned
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Job related to staff position within a Research Infrastructure? No Offer Description ASML Photolithography machines play a central role in the production of ever smaller and faster microchips and
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Job related to staff position within a Research Infrastructure? No Offer Description ASML Photolithography machines play a central role in the production of ever smaller and faster microchips and