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of large-scale machine learning models (e.g., LLMs) in a meaningful way, we, therefore, need new scalable methodologies that can efficiently and accurately capture, represent, and reason about uncertainties
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machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
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standards. About the research project The postdoctoral project will focus on precision tests of low-energy strong interactions via the ab initio modeling of open-shell, nuclear many-body systems and Bayesian
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to numerical analysis and optimization, as well as mathematical statistics and machine learning. The centre offers a lively academic environment where colleagues from many parts of the world come together
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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
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simulation model for autonomous shipping in restricted waterways. This position offers unique opportunities for collaboration within the Marine Technology Division at Chalmers University of Technology
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We are looking for a highly motivated and skilled Postdoctoral researcher with cross-disciplinary expertise to help develop a digital twin ship simulation model for autonomous shipping in restricted
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methods to rigorously assess the safety and effectiveness of medications in real-world patient populations. Defining individualized treatment strategies: Leveraging traditional and causal machine learning
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science. You will be part of a dynamic research group with expertise in Earth Observation, geoinformatics, and machine learning, offering an excellent environment for advancing your research and building
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analysis, statistical modelling, linear mixed models, and machine learning among others. The position is well suited for an individual interested in quantitative genetics and data analysis that wishes