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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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Computational Biophysics/Chemistry (see also https://constructor.university/comp_phys ). The PhD position is focused on efficient algorithms for the simulation of non-adiabatic exciton transfer dynamics in light
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exploration, optimization, and search algorithms in extremely complex and enormously large spaces motivated by physics and chemistry (RL, BO, Large-Scale Ansatze, …) AI-driven discovery of hardware for some of
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. This is because experimental techniques to solve structures of protein complexes favor more stable interactions with larger interfaces and because we lack efficient algorithms to compute similarity between
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) determine, using sensitivity analysis, impact of the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms
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of algorithms and digital neuromorphic hardware is an additional avenue for enhancing the efficiency of the methods. In this context the research will explore digital, event-based implementations
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the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we