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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given
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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given
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, uncertainty-aware decision-making, and efficient inference and model updates under latency, memory and energy limits. The precise research focus will be developed with the successful candidate within
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and corrective feedback. You will apply advanced algorithms for machine learning, multimodal biosignal processing, and human-state inference, working with shared-control strategies and electrotactile
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic data. The aim is to enable meaningful analyses, such as identifying disease
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fibrillation. Your work will involve applying Mendelian randomization and other causal inference methods to uncover potential genetic pathways contributing to disparities in stroke risk. The project combines
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analysis, causal inference with machine learning, and deep learning for various health-related domains. The Global Pathogen Analysis Platform (GPAP) is a new international initiative to strengthen global
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contribution Research approach/method. Literature references Tentative time plan Societal and scientific relevance and impact of research project Please note that we request the above to infer PhD applicants