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, Python, and SAS or Stata) • Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling, experience with Medicare claims data • Clear scientific writing and
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interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict the existence of undiscovered small molecules that are likely to be
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and temporal patterns from multisource data, spatiotemporal data analysis and mining and model learning and physical parameter prediction. Responsibilities consist of conducting computer modeling
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Description Whether behaving as a solid, fluid or gas, powder is a state of matter that is difficult to model on a large scale, specifically in industrial equipment. The sizing of powder agitation devices and
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financial goals through enrollment data analysis, predictive modeling, and decision support. This role combines deep analytical expertise with business acumen to transform complex data into actionable
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 2 months ago
prediction (e.g., AlphaFold2), allosteric signaling remains poorly understood, largely due to the scarcity of dynamic data. Our group recently developed: DynaRepo, a database of molecular dynamics trajectories
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and refine the RG-based model to enhance its biological interpretability and robustness across different tumor types; to extend the model to simulate and predict solid tumor response to innovative
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that enhance the development and evaluation of advanced analytical models using health data. This includes methods for prediction, explainability, prediction under intervention, algorithmic fairness, transparent
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creating a unified data framework for microbial carbon dioxide conversion and establishing a predictive AI modeling. Your profile The candidate is required to have a strong background in AI/machine learning
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optimisation. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model