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Your Job: As a PhD candidate you will develop and deploy an artificial intelligence (AI) driven approach to streamline high-throughput experimentation (IMD-3: Institute of Energy Materials and
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
real-time analysis of electrochemical processes, developed in the Department of Electrocatalysis, will be applied by You to discover and develop novel Organic Electrosynthetic Protocols. Your tasks
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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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: 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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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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, which will be used to inform the digital twin for regenerative agriculture developed in action field 1 of ReGenFarm. work in an interdisciplinary team with other doctoral and postdoctoral researchers
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workshops, career development, and networking Join a dynamic institute hosting over ten atmospheric chemistry research groups, offering rich opportunities for collaboration and learning Extensive
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
self-assemble from a number of interacting single-stranded DNA molecules. An accurate prediction of DNA structures still remains difficult, which significantly slows down the development of new desirable
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occur, and how can the overloading of individual regions be counteracted? Your contribution to scientific analysis: Further develop existing energy system models in Python to accurately map and analyze
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support the development of a European energy system model by benchmarking future technologies and optimizing their representation within the FINE optimization modelling framework ( https://github.com/FZJ