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algorithm to reduce noise in dual-tracer PET imaging and improve the quantitative accuracy of dual-tracer PET scans. Your Job Simulation of PET data using existing simulation packages. Development
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experimental settings. In addition to fieldwork, the PhD candidate will contribute to the development of novel inversion algorithms for EMI and GPR based on full-waveform inversion techniques. These methods aim
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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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experimental systems for cryogenic measurements Development of a microwave quantum control & readout stack Development of Python code to operate quantum systems Detailed experimental characterization
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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devices Develop hardware-aware machine learning models incorporating electronic and optical device constraints Design and implement hardware-efficient training methodologies for machine learning systems
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical