56 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "St" "St" positions at Linköping University in Sweden
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of Computer and Information Science , within Linköping University . Your work assignments As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your
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of existing bioinformatic workflows and development of new pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation
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methods to provide explainable outputs from AI models in presence of attacks on the models or data, and scalable methods that move beyond feature attribution aiming for root cause analysis and decision
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research at LiU: https://liu.se/en/research/cybersecurity The employment When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each
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through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software
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. Information about the workplace: https://liu.se/en/organisation/liu/ifm https://liu.se/en/research/m2lab The employment This employment is a temporary contract of two years with the possibility of extension up
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communication limitations, adversarial conditions, continual and adaptive learning in dynamic environments. The research will combine tools from distributed optimization, stochastic approximation, information
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. A successful candidate should have very good knowledge in quantitative methodology and related analysis tools, in particular very good knowledge in analysis with registry or survey data from various
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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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learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce inference and deployment costs