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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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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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their glial niche. Genetically encoded, fluorescent metabolite sensors will be used to study the underlying metabolite dynamics. The work will also incorporate various molecular biology techniques, as
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their glial niche. Genetically encoded, fluorescent metabolite sensors will be used to study the underlying metabolite dynamics. The work will also incorporate various molecular biology techniques, as
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group focuses on developing strategies and algorithms to quantity biologic effects of particle radiation based on underlying physics, biology and physiology. Within the BMFTR funded project “BIOMICRO
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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- looking. Sensor technology plays a key role in this transformation, enabling real-time monitoring, automation, and intelligent decision-making. Despite these needs, many water treatment processes still rely
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meteorological stations and occasionally supplemented by opportunistic sensors. The integration of diverse opportunistic sensor data sources [4], such as private weather stations, further broadens the capability
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spectroscopy. The present project will involve the following steps: • Development of scanning probe near-field sensors based on solution-synthesized metallic nanoparticles • Operation of an existing setup