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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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(rhizotron facility) and field trials. In addition to field applications, novel inversion algorithms for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable
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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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for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel
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Your Job: Quantum computers will play a crucial role in the development and optimization of battery materials in the future. In this project, you will develop innovative quantum algorithms
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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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this project, you will develop innovative quantum algorithms for the accurate calculation of materials properties. You will combine methods from quantum informatics and solid-state physics to describe
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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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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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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