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) on Foundation Models and/or Deep Learning for Imaging Problems. The Professorship for Machine Learning at TUM works on machine learning, artificial intelligence, and information processing. The current focus is
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of models in existing simulation software conducting numerical studies, also on HPC systems Further specific tasks can be tailored to the attitude and interests of the PhD students/postdocs. Requirements
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:100% of the full-time weekly hours Tasks: The PhD student will be responsible for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs
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to challenging questions in the field of computational material design, especially with the help of CALPHAD-based methods. For further development of our simulation environment (https://github.com/cmatdesign
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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of bespoke probabilistic models and/or evolutionary simulations, robust knowledge of and an affinity towards mathematical, computational or probabilistic modeling are important. Further skills in modeling and
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, sense amplifiers or memories Implement and verify circuit layouts through simulation Utilize advanced CMOS technology nodes (28nm, 22nm, and below) Develop behavioural models for circuit verification
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the project “Modeling Great Ape Signaling Behavior” under the auspices of the Collaborative Research Center “Common Ground” (CRC1718), which is funded by the German Research Foundation (DFG), at the University
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applied in Africa’s livestock sector. This work will involve modelling analyses using methodologies to be developed by the candidate in collaboration with their supervisors. This is one of two positions
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schematics, layout, test benches and simulation Design blocks as Op-amp, LDO, VCO, PLL, voltage or current reference circuits Develop innovative analog design solutions to enhance signal integrity, noise