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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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Professor will be responsible for teaching and research in some of these areas: computer architecture, media, database, algorithms, parallel and distributed systems, etc. Only shortlisted candidates will be
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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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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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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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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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causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal
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focus on a variety of key technologies like Distributed IT Systems, Internet of Things, IoT, Cybersecurity, Data Science, Artificial Intelligence (AI), Blockchain Technologies, Quantum & Photonic
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focus on a variety of key technologies like Distributed IT Systems, Internet of Things, IoT, Cybersecurity, Data Science, Artificial Intelligence (AI), Blockchain Technologies, Quantum & Photonic
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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