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promoters. You will train and evaluate predictive models in model/crop species with different levels of genome complexity. You will work very closely together with your dry-lab colleagues for data processing
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of scientific principles and theories. Maintains substantial knowledge of state-of-the-art principles and theories. Develops advanced analytical models and systems and provides solutions and analyses
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patient-derived xenograft (PDX) models to discover novel therapeutic targets and biomarkers. The postdoc will have access to state-of-the-art facilities, multidisciplinary collaborations, and opportunities
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, while contributing to development efforts from algorithm design and testing, synthetic data generation, user interfaces, and model deployment. Initial appointments will be for one year with
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groups led primarily by Professor Tarek Abdoun and Professor Mostafa Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development
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experience with numerical modeling codes such as ASPECT or Underworld Geodynamics (UWG). Preferred Qualifications: Familiarity with landscape evolution modeling tools like Fastscape or BADLANDS is highly
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. Research opportunities will focus on the use of novel modeling tools for hydrology and water resources systems, with an emphasis on machine learning and remote sensing, with a focus on developing detailed
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: Using biogeochemical evolutionary models to simulate lifeless and inhabited worlds, and Developing disequilibrium-, redox-, and information-based metrics to understand and quantify the influence of life
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will be expected to dedicate your time develop a high-biofidelity, high-resolution computational rat model with dual applications: i) advance the mechanistic understanding of brain injury by linking
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | about 20 hours ago
multi-expert segmentation databases. The postdoctoral fellow will focus on integrating segmentation variability into deep learning models, with the goal of assessing prediction reliability and enabling