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robustness, fairness, and accessibility. You will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results through publications and
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utilizing Python and web-stack technologies (such as JavaScript), to translate theoretical models into functional, testable software in close collaboration with our core research team. Beyond software
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of machine learning such as robustness, fairness, and accessibility. You will design and run reproducible experiments, measure relevant resource metrics, implement prototypes in Python, and communicate results
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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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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental
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image analysis, deep learning as well as mathematics. You have substantial expertise in programming, especially in Python and Matlab. You are independent, meticulous and work efficiently. Since
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of novel 2D materials (e.g., thin-film deposition by PVD and CVD). Proven programming skills (e.g., Python) for instrument control and data analysis. You are a highly motivated and independent researcher