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Field
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causal inference in heterogeneous data environments, addressing the challenge of enabling trustworthy causal analysis across distributed datasets while preserving privacy. The successful candidate will be
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specialized algorithms supported on solid theoretical foundations and with a focus on challenging aspects of very high-dimensional datasets, such as datasets encountered in the computational biology and
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distributed energy resources (DERs). Design & develop optimization algorithms/tools to plan the deployment of DERs such as energy storage systems (ESS), photovoltaic generations (PV), electric vehicle charging
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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
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energy resources. The expected outcomes include technical advancement of distributed algorithms for managing energy resources at customer premises. The benefits include more resilient, secure, private, and
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sites (k-nearest neighbor algorithm, centroid models, distribution models, etc). We are also expanding on our previous work applying community detection methods, such as modularity maximization and
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-scale Logistics. Our vision is that local production, distribution, and reuse of goods using robot swarms will enable a more sustainable future through reduced transport emissions and waste. This vision
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research subject for this position is development of distributed processing strategies and algorithms for Large Intelligent Surfaces, including both joint baseband processing and synchronization across
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intelligence (AI) and machine learning(ML). Duties This position combines knowledge of the Earth observation (EO) domain (EO instruments, EO data, EO algorithms, modelling, etc.) and AI/ML, as well as data
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conditions are caused, transmitted, and prevented, as well as how they are distributed throughout the population. Such information plays a critical role in guiding policies and other evidence-based strategies