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formation and how local dose is distributed. In the longer perspective, this knowledge will support optimization and translation of bioelectronic implants towards clinical application. In this project, you
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the Ph.D. Our recent works on AI privacy and security: Practical Bayes-Optimal Membership Inference Attacks, NeurIPS 2025, https://arxiv.org/pdf/ 24089 Secure Aggregation is Not Private Against Membership
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects
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. The employment requires strong subject knowledge in optimization, mathematical modeling, and quantitative analysis. You are a problem solver who works well with complex issues, understands complicated written
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principles for transceiver frontend design, including data converter solutions. Expected outcome is a disruptive and novel approach to co-optimized radio transceiver design with measured and verified state