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. MINDnet aims at addressing the challenge through a holistic optimization - from individual computing devices to the overall architecture, including a focus on applications, and training methods - across
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local activation), multi-timescale adaptation (local memory), and stimulus-specific adaptation (multi-task processing). While the co-optimization of dendrite-inspired functional circuits with emerging
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project, you will help design, simulate, and optimize these next-generation communities — making clean, local, and intelligent energy systems a practical reality. Your key responsibilities include
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the inductive heating of heterogeneous catalysts as an integral advantage. Your focus will be on the investigation, development, and optimization of this new process in a continuously operated reaction
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-efficient processing capabilities. MINDnet aims at addressing the challenge through a holistic optimization - from individual computing devices to the overall architecture, including a focus on applications
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on model surfaces In situ and operando (surface) X-ray scattering studies of catalyst/polymer interfaces Design, optimization, and testing/benchmarking of reactors for in situ and operando studies
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linkages based on numerical simulations and to transform them into AI- and ML-ready information to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit
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engineering Willingness to work in laboratory and cleanroom environments Ideally, initial experience in a technical or scientific environment (e.g. cleanroom, laboratory) Knowledge of solid state physics and/or
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solution to support such a demand by getting inspired by the brain’s powerful and energy-efficient processing capabilities. MINDnet aims at addressing the challenge through a holistic optimization - from
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degree in chemistry, electrochemistry, technical chemistry, chemical engineering or a comparable field (e.g. engineering related to process engineering or chemistry) Good knowledge of electrochemistry