Johan Schött is a Senior Engineer with a decade of experience bridging computational physics and high-performance software, now focused on HPC at Mycronic. He earned a PhD in computational quantum physics from Uppsala University after training in engineering physics at Chalmers and ETH Zurich, and has applied advanced electronic-structure methods (DFT, DMFT) to magnetism, x‑ray spectroscopy and ill‑posed inverse problems. Johan has a strong track record of implementing and benchmarking new functionalities in scientific codes, developing CT and XRF analysis algorithms with machine learning at Orexplore, and publishing peer‑reviewed research. Comfortable moving between hands‑on code, large-scale simulations and data analysis, he combines rigorous theoretical insight with practical engineering to deliver production‑grade scientific software. An uncommon strength is his work on improving analytical continuation (Padé sampling) — a niche, high‑impact algorithmic advance that showcases both numerical creativity and deep domain knowledge.
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