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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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about working with us Work at Lund University | Lund University Ready to shape the future of research? Find more reasons why Lund University and the HT Faculties is right for you here , and learn more
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Work at Lund University | Lund University Ready to shape the future of research? Find more reasons why Lund University and the HT Faculties is right for you here, and learn more about Working in Lund
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these plants. The post-doc is expected to build upon existing in-house tools and, where applicable, enhance them by means of AI (machine learning) and data-driven methods. These models are aimed to support
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Information Technology conducts broad research in cryptography, computer security, wireless and wired networks. The Security Group has around 20 members. The main research areas are cryptography, privacy, and
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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
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machine-learning tools. Data analyzed include precursors such as volatile organic compounds, aerosol number and mass concentrations, chemistry, biological particles, cloud and ice condensation nuclei, light
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials