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. This project aims to address these challenges through the development of adaptive learning frameworks for non-stationary environments. In particular, we focus on continual learning, where models are updated as
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between people and pave the way for science, creativity and development. Therefore, we welcome applications from people with diverse backgrounds and perspectives. Where to apply Website https://web103
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screening approaches. The PhD student will develop and apply analytical workflows to characterize complex food matrices. The project includes i) developing and optimizing screening workflows; ii) improving
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well as developing methods to study and characterize these processes. Within the research group, we highly value a positive work environment characterized by respect and care in our relationships with one another. We
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to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for DataDriven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to
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, and research literature in a structured and reflective manner. In addition, you should have well-developed problem-solving skills and be able to analyze complex research questions, identify relevant
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position within a Research Infrastructure? No Offer Description Description of the workplace Within the Centre for Analysis and Synthesis (https://www.cas.lu.se/ ) at the Department of Chemistry, we conduct
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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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transport in solid‑state polymer electrolytes for Li‑ion batteries. The research will focus on developing new and computationally efficient strategies for multiscale modelling, enabling systematic studies
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. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ Background and description of tasks Our group develops new single-cell multiomic methods to characterize microbial