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Field
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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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selectivity and permeability and ultrahigh water permeability combined with high salt rejection. The objective of this work is to construct atomistic models of MOFs/Polymers and Artificial Water-Channel
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on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities
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size and density approaches. Demonstrated experience in infrared spectroscopy of soils and use of multivariate statistical models to predict soil properties. Experience in process-based models
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University Abu Dhabi invites applicants to apply for the open Post-doctoral Associate position to perform primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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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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development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for identifying high risk individuals and groups. The PDRA will translate conceptual
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environment. Development of models to diagnose and predict battery performance and ageing. Participation in national and international research projects related with energy storage and its integration in