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The Section of Biostatistics is looking for a postdoc to develop statistical methods for inference on causal effects in studies affected by non-random participation, particularly self-selection in
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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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inference with a focus on developing and applying statistical, machine learning methods, and AI to analyse population-scale health data and omics data. The position has a start date as of 1 January 2027 or as
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models, integrating federated learning or decentralized training schemes with differential privacy and secure aggregation to prevent training data memorization and membership inference attacks
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training data memorization and membership inference attacks. Cryptographic protection mechanisms for collaborative inference and fine-tuning, employing secure multiparty computation or homomorphic encryption
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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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statistics have been a key element in your work. Experience with data handling and flexibility in using a wide range of statistical methodologies, both frequentist and Bayesian. Demonstrated proficiency in
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statistics have been a key element in your work. Experience with data handling and flexibility in using a wide range of statistical methodologies, both frequentist and Bayesian. Demonstrated proficiency in