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pulses develop numerical codes to calculate the system dynamics by solving partial differential equations (e.g. Schrödinger equation, von Neumann equation) model the coupling to lattice vibrations (i.e
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models, and reinforcement learning (RL), which is data-driven, are two powerful control techniques. MPC techniques are well-established, while RL techniques are gaining popularity due to increasingly
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. The system will include: A very compact, ultra-low-power analog front-end (AFE) to sense neural signals. An on-chip neuromorphic processor to convert the neural data into spike-based encoded data and
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the power supply leading to significant enhancement of grid resilience. Collaborative operation of the local energy systems will be studied to leverage differences in storage capacity and renewable energy
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small-scale processing sector. By joining this project, you will contribute to the development of AI-powered tools that predict non-compliance, improve food safety monitoring, and ultimately protect
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. Accordingly, we require documented skills within theoretical and applied statistics and the ability to program in R. Also, the applicant should have the ability to communicate research findings in teaching
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, along with strong communication skills. Ability to independently pursue complex tasks while actively contributing to a collaborative, interdisciplinary research environment. Strong motivation to achieve
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models, and reinforcement learning (RL), which is data-driven, are two powerful control techniques. MPC techniques are well-established, while RL techniques are gaining popularity due to increasingly
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decision-support or safety-critical systems. Knowledge of Cyber-Physical Systems, Sensor Fusion, or Risk Management is an advantage. Excellent programming skills (Python, C++, or similar). Ability to work in
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Job Description Are you passionate about the green energy transition? Do you wish to contribute to the development of solutions to support the increasingly renewable power grid? This fully funded 3