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acoustic waves are generated and transmitted in wells. The LeDAS project aims to overcome these challenges by combining physical modelling, advanced signal processing, and machine learning in a hybrid
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Are you interested in developing interpretable AI models for the next generation of green syntheses? Do you have experience in AI/Machine Learning, or computational modelling of organic reactions
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques for generating high-resolution climate projections. In addition to developing
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performance simulation software (e.g. EnergyPlus, CFD, COMSOL) as well as in programming languages like Python and familiarity with AI/machine learning frameworks (e.g. PyTorch, TensorFlow). Applicants
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Full-time: 35 hours per week Fixed term: 6 months Develop machine learning of cellular automata models with applications to in vitro developmental biology. The Opportunity: This post is full-time
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an active role in data management, processing, and quantitative analysis (e.g., longitudinal and multilevel modelling, time-series or high-frequency data analysis, machine learning or predictive
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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and energy materials. Preference will be given to those with knowledge of computer programming, AI and/or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study