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multiome RNA-seq, ATAC-seq and massively parallel reporter assays (MPRAs) for unbiased genome-wide analysis for understanding the phenotypic plasticity in different cancer cell states. Work tasks The work
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of dendrochronology techniques, from the research plan, field work and laboratory, and interpretation and publication. He/she should be comfortable with the R and/or Python languages. Experience in handling large
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preference for former and recent deep learning models (GNNs, Transformers, etc.). Strong programming ability (Python preferred) with hands-on skills in AI and Deep Learning frameworks (e.g., Pytorch
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models to study semantic relationships and changes in language during different periods of conflict collaboration with an interdisciplinary team performing statistical analyses active role in writing
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programming and software development. Familiarity with Python and statistical computing libraries, like PyTorch or JAX, etc., would be preferred. You are a motivational teacher, with an encouraging teaching
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identifying high risk individuals and groups. The PDRA will translate conceptual frameworks into mathematical models of complex cross-scale disease dynamics across different epidemiological scenarios. A key
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qualifications: Strong experience in programming using Python, R, or other languages Research experience in remote sensing of cover crop, crop type classification, and crop biomass Insight into global
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modelling tools are required. Robust modelling and programming abilities (e.g., Python) are essential prerequisites. Experience with VIC (or similar hydrologic models), GIS, and large-scale computing
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of differing background aerosol composition and to relate these distributions to in-cloud turbulence and cloud brightening. The Doppler lidars at the pier and on Mt. Soledad characterized sub cloud TKE profiles
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biological data. · Proficiency in R and/or Python for data analysis and visualization. · Experience working with large datasets in an HPC or cloud computing environment. · Demonstrated ability to work