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years based on satisfactory performance and availability of funding. The Postdoctoral Research Scholar will join a team, under the direction of Prof. Michael Woodford, who studies the cognitive
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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that are commonly used today. Using the improved noise models, machine learning methods will be used to enhance the segmentation of EEG data into auditory signal and background activity allowing for refined control
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build upon the existing Alpha framework (originally developed to model protein quality, see https://alpha-tool.eu ) and further develop it as a research-oriented modelling and demonstration platform
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relational database environments Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses) Apply machine learning approaches for predictive modeling
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breed x system interactions. Including e.g. milk-based parameters according to other WPs, production system specific early prediction models for the control of endoparasites will be developed
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—influence emotional activation, cognitive information processing, and subsequent economic behavior. The empirical part of the work will be based on a controlled laboratory experiment in which auditory stimuli
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on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field
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. Key Responsibilities: Develop and implement perception and control algorithms for robotic arms and embodied AI systems. Assist in integrating multimodal AI models (vision, language, force sensors) with