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segmentation, tracking, classification, and more. You will utilize probabilistic models to produce uncertainty-aware predictions across scales. This role requires deep knowledge of the underlying models and
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these models for scalable vision tasks, instance segmentation, tracking, classification, and more. You will utilize probabilistic models to produce uncertainty-aware predictions across scales. This role requires
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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variants as functional and assess their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in
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., feature engineering, spatiotemporal modeling, Bayesian calibration, ensemble methods) to improve prediction accuracy and uncertainty quantification. Disseminate research findings through presentations
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. This includes exploring the use of digital twins for bioreactors and deploying AI driven predictive models to improve optimisation, consistency and overall yield. The main focus for this role is to work with the
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Your Job: You will be a member of a consortium of leading research institutes and an industry partner. Your task is the build-up of a predictive model for tandem cell stability. Your tasks in detail
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: Portugal Website: https://www.lnec.pt Email: recrutamento@lnec.pt The scientific research, technological development, management and communication of science and technology activities shall be carried out
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short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning
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have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models for Alzheimerâ™s, Parkinson, and Dystonia as well as to identify novel