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addressing complex fracture problems, and you are comfortable translating detailed micro and mesoscale mechanisms into models that can be used for engineering design and assessment. You have a track record of
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other environmental stresses. However, identifying the genes underlying these complex traits remains challenging. This project will combine plant genomics, artificial intelligence, and evolutionary
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thereafter. Explaining complex AI models is a key challenge for ethically responsible AI. Explainable AI (XAI) research aims to provide relevant information to assist developers and users in analyzing AI
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Engineering. The position is closely connected to our activities within developing high-fidelity models that capture the coupled thermal, chemical and electrochemical phenomena governing system performance
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environments; experience coordinating complex workflows (e.g., between wet lab, proteomics and dry lab) and maintaining structured project documentation. The lab places strong emphasis on teamwork, where
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control structures and advanced control methods and are comfortable working with mathematical models of complex technical processes. Experience with modelling of thermo-chemical or process systems
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to pharmacological intervention. We will combine studies in GPR6 knockout mice with pharmacological approaches in rodent models, including Flinders Sensitive Line (FSL) rats, a model of depression. By linking
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of two years. About the Project The successful candidate will advance the algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems
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with complex pre-clinical in vitro and in vivo models. Programming expertise in Python and R is desirable. As a person, you have good interpersonal skills, are inclusive and team-oriented and able
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. The tasks include battery cell characterization and modelling based on laboratory tests, and development of algorithms for estimating the charge level, health, and power capability which includes robustness