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Field
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transcriptomic data, that will be integrated with clinical metadata and whole-genome data for developing machine learning models to identify and predict patient factors driving toxicity response and sensitivity
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effects and their coupled interactions. In effect, this a complex problem that needs the application for AI / machine learning to enable guided, efficient and effective optimization of the CHIPLET
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. Knowledge of statistical methods commonly used in single-cell and spatial omics, such as Seurat and Scanpy. Experience with machine learning models, such as transformer and diffusion models. Strong written
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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian
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written and oral English. Experience from one or several of the following areas is an advantage: Programming, image processing and machine learning. Magnetic Resonance Imaging. Laboratory experience from
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machine learning. Magnetic Resonance Imaging. Laboratory experience from porous media research related to physics and/or chemistry. Personal and relational qualities will be emphasized. Motivation
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enhance our contribution to society. Responsibilities* The successful candidate will work on projects performing analysis of PET neuroimage data, including kinetic modeling and multimodal data synthesis
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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and machine learning to optimize treatment conditions. Contribute to the development of reproducible stress priming methods and assist in transferring knowledge to agricultural stakeholders. Required