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system analysis. The work includes integrating AI methods with energy system models as well as developing methods for transparency, explainability, and uncertainty management, with a particular focus on
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large-scale omics datasets, develop and apply statistical methods and interpretable AI models, and contribute to the identification of biological markers and molecular mechanisms associated with disease
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communication skills in Swedish, Strong knowledge of electric power engineering, power electronics, and power system analysis, Experience of modelling, simulation, and experimental work. In an overall assessment
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tissue formation, and limited spatial precision. Building on our recent advances in enzyme-triggered electrode fabrication in living systems (Strakosas et al., Science, 2023, https://doi.org/10.1126
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strong interest in e-health and digital health services, AI including large language models, and interdisciplinary work. The doctoral studentship forms part of Samverkan e-hälsa , a long-term collaboration
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characterization of HIV RNA/DNA in primary material as well as model cells using Oxford Nanopore sequencing and complementary molecular and computational approaches. The doctoral student will help develop, optimize
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applications in different project contexts. This may include data analysis, modelling, literature reviews, and interaction with relevant stakeholders. The results are expected to contribute increased knowledge
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knowledge-based models and practical guidance that strengthen audit firms’ capabilities in delivering sustainability assurance, support the work of regulatory authorities, and enhance trust in sustainability
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features