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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will you be part of that change? Then join us in
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- experience in programming (e.g. Matlab and/or Python) - experience in fieldwork - proficiency in scientific writing - skills in communicating with people who have different backgrounds Your workplace
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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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techniques, and stochastic optimization. Additional knowledge of machine learning and experience with programming in Python and PyTorch would be considered an advantage. You are experienced in conducting
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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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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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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