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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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trustworthiness modeling on multimodal data and machine learning models. The Department of Computing Science has been growing rapidly in recent years, with a focus on creating an inclusive and bottom-up driven
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growth. The 60 doctoral students within the department are a diverse group from different nationalities, backgrounds and fields. We offer very good employment conditions, and administrative and technical
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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pilot-scale facilities. The characterization includes all conventional methods for product quality, and detailed studies to understand the connection between the new process conditions and the resulting
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absence due to illness, parental leave, appointments of trust in trade union organisations, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment
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special conditions earlier doctoral degrees can be considered. Special conditions include leave due to illness, parental leave, clinical service, positions within trade unions or other similar circumstances
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climate conditions, ecology and human mobility. There is a collaborative link between Umeå University and Lund University. Description of duties The postdoctoral researcher is expected to engage in advanced