10 postdoctor-simulation-optimization PhD positions at Linköping University in Sweden
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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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, 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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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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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
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simulation of time dependent non-linear PDEs has emerged as a key technology. A main task of this employment is the development and analysis of numerical methods for wave propagation problems. Particular focus
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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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of previous degrees, a list of peer-reviewed publications (if any), and contact details for at least two references. Copies of degree certificates and undergraduate transcripts. A copy of your Master’s thesis
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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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, simulation, layout, and PCB design using advanced CAD tools, as well as experience in the measurement and characterization of integrated circuits. Strong communication skills and proficiency in both written
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weather forecasting to cardiovascular medicine. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning