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analysis in medicine. Experience of software version control with Git, typesetting with LaTeX, use of Linux computers; Experience with graph-based methods, and graph convolutional/neural networks; Experience
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research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows, innovative technologies for biomass conversion, neural network systems, and artificial
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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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regulators of disease onset and progression. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating
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. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating multimodal data with clinical information
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, innovative technologies for biomass conversion, neural network systems, and artificial intelligence for more efficient mathematical and computational approaches. Subject description The work focuses on
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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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-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