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-based environments for high-performance data analysis. Knowledge of biological network inference, causal modeling, and graph-based AI approaches. Experience in multi-modal data fusion, representation
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staff position within a Research Infrastructure? No Offer Description Summary of Activities: 1. Development of a conceptual description of offshore production chains through knowledge representation
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must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining. • Strong background and knowledge in cell culture, microscopy, tissue
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fundamental challenges in multimodal representation learning by developing novel approaches to align distinct embedding spaces from speech and sign language modalities. Sign languages encode information through
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push the frontiers of analogy between real-world volcanic plumes and small-scale experiment, including a representation of atmospheric stratification, sheared atmospheric wind, and multiphase plume
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collaboration with Olof Lagerlöf’s group. The postdoc will image (and influence with opto-chemogenetics) the network representation of stimuli of different economic/hedonic value and its plasticity with two
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representation theory. • Some experience with SageMath, polymake or other related mathematical software. • Proficiency in English, both written and spoken. Knowledge of German is not required. In addition
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research in causal representation learning, inference, and discovery; advance explainable models that enable discovery of image-based markers predictive of future disease evolution; and build fair, robust
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candidate with a strong background in semantic data modeling and knowledge representation, ideally in engineering or scientific domains. You will take the lead in designing, extending, and implementing
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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and