10 algorithm-development-"the"-"University-of-Michigan---Flint" PhD positions at Forschungszentrum Jülich
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Your Job: The conventional, manual co-design of algorithms and hardware is slow and inefficient. Our group develops methods and tools to automate the co-design process. The core of this project is
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efficiency. Your Job: Develop and apply meta-optimization that can automatically search for the best algorithm-hardware pair Tackle the challenge of computationally expensive meta-optimization procedures by
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: Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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Your Job: The accelerated development of advanced materials is essential for addressing major challenges in energy, mobility, and sustainability. Traditional trial-and-error methods in materials
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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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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Your Job: Random unitaries are a ubiquitous tool in quantum information and quantum computing, with applications in the characterization of quantum hardware, quantum algorithms, quantum cryptography
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to co-design algorithms and circuits to develop efficient neuromorphic hardware, tailored to target tasks. In detail, you will: develop circuit-plausible training/inference algorithms and analyze in