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characterized by smart technology (including artificial intelligence, robotics, and mixed reality) that augments people, drives innovation responsibly, and fosters adaptive, inclusive, and empowering workplaces
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: The development of computational models to represent structural performance using commercial and research software tools The development and validation of machine learning and artificial intelligence models focused
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Methods for Enhanced Market Intelligence, Automated Market Research Summaries via RAG-enabled AI. The successful candidate will extensively explore and analyse the suitability and potential benefits of new
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
candidate is expected to: Build and evaluate chemical databases based on experimental and computational data. Establish predictive models based on artificial intelligence methods. Utilize the developed models
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.) together with advanced artificial intelligence methods. The main objectives are to: ➔ design and experiment with multimodal AI models to improve the diagnosis and prediction of battery aging; ➔ explore and
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understanding of electro-active artificial muscles, or other stimuli-responsive smart materials. We are looking for someone with a strong passion for interdisciplinary research, collaborative teamwork, and
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undergraduate researchers in the Howland Lab and MIT Parsons Laboratory. Meeting escalating energy demand from emerging uses, such as artificial intelligence (AI) and data centers, will require an unprecedented
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Does this sound like you? You hold A PhD degree in Artificial Intelligence, Data Science or Information Science, Language and Speech Technology or a closely related field. You have extensive experience
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related to staff position within a Research Infrastructure? No Offer Description Call for Postdoctoral Researchers in Artificial Intelligence (AI) – Focus on Large Language Models (LLMs) for Predictive
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to advance artificial intelligence (AI) methods that improve the reliability of clinical prediction models when faced with data drift, bias, and fairness challenges. The research will involve developing deep