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at the Swedish University of Agricultural Sciences (SLU) in Uppsala, Sweden. Research in the Evolving Networks lab (evonetslab.github.io) centers around understanding how species interactions change over time and
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Statistics is offering a postdoctoral scholarship within the project “Phase transition in aggregation processes and network models”. The scholarship is full time for two years with starting date 1 January
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data reflect real‑world disease phenotypes. Advanced analytics: apply AI and machine‑learning techniques (e.g., graph neural networks, multimodal transformers) to uncover novel biomarkers and generate
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experiments, samples from world-unique CO2 experiments, cutting-edge NMR spectroscopy and isotopomer analysis (doi 10.1111/nph.20358). Two postdocs will work together to conduct plant ecophysiology experiments
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-smokers. The successful candidate will have a leading role in the analysis of bulk- and single cell RNA-seq data, miRNA seq data from scarce particle samples, multi-omics integration and network medicine
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and programming are highly meriting, especially in gene regulatory networks, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially CRISPR screening, is highly meriting
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architecture and forest structure analysis. We expect you to develop innovative research and report findings in high-impact scientific journals. We expect you to network and collaborate with remote sensing
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the laboratory and with collaborators in various networks are essential. Previous post doc experience and a strong publication record are strong merits.] Postdoctoral scholarships may be established for foreign
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architecture and forest structure analysis. We expect you to develop innovative research and report findings in high-impact scientific journals. We expect you to network and collaborate with remote sensing
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required. Proficiency in statistics and programming are highly meriting, especially in gene regulatory networks, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially