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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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-atomic potentials (e.g. pro-fit, MLIP-3). Knowledge of meso-scale models such as cluster dynamics (e.g. Xolotl, Centipede), object-kinetic Monte Carlo or similar. Proven commitment to proactively keeping
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injection, ion transfer, and structural dynamics in realistic and model systems for battery materials. The position will span experimental efforts at large scale X-ray facilities, handling and reduction
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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, western blot analyses, and qRT-PCR. • Expertise with immunofluorescence high-resolution imaging and live-cell imaging. • Experience with preclinical models of auditory and vestibular diseases. • Experience
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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, digital twin, geospatial mapping with vehicle and UAV mounted remote sensing systems or robotic systems, crowd simulation, reinforced learning, flood modeling and impact analysis, complex system modeling
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modeling, physiological signal analysis, and innovative neuromodulation strategies for neurological disorders such as epilepsy, chronic pain, and autonomic dysfunctions. Primary Responsibilities 35
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epidemiology to understand RNA metabolism. Perform stochastic simulations to analyze model behaviors. Fit the model parameters to empirical RNA expression and RNA-protein binding data. Predict outcomes
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spectrometry-based metabolomics, isotope tracing, and lipidomics, to dissect metabolic rewiring in lung cancer. The research will integrate patient-derived models, organoids, and in vivo tumor models. Lab Focus