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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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dynamic systems, control engineering, and applied mathematics? Do you want to develop cutting-edge control algorithms for the security and resilience of cyber-physical systems? We welcome you to apply for a
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excitations and excitonic effects using advanced Wannier-based methods * Quantum transport in polymer materials with electron–phonon coupling Full details and application instructions: https://www.ch.nat.tum.de
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The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic architecture of important crop traits, such as grain yield, adaptation
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Hands-on experience with machine learning algorithms and developing/testing complex software systems Basic understanding of hardware/embedded software development Additional Requirements (MSCA Eligibility
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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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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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mentoring for building a career in academia or industry Professional development through JuDocS, including training courses, networking, and structured continuing education ( https://www.fz-juelich.de/en