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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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, generative diffusion models, flow models, optimal transport, stochastic filtering, sequential Monte Carlo, Markov chain Monte Carlo, and Bayesian inference and inverse problems is strongly advantageous. Your
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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