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between pollution control efficiency, electrochemical yield, and energy recovery potential; • Develop coupled electrochemical and hydrodynamic models to predict process behavior; • Participate in
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model disease trajectories to identify risk factors and improve disease prediction and prevention. Responsibilities will include conducting detailed analysis of multi-modal data from the UK Biobank, in
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prediction, control systems (e.g., PLC), ML model deployment, and time series sensor data analysis are assets. • Excellent communication skills and ability to work in a multidisciplinary team. Duration of
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will rely on: Developing machine learning-based surrogate AI models (physically informed neural networks) to predict the evolution of calcium carbonate precipitation rates associated with the reduction
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Medicine Postdoc Appointment Term: Fixed term for one year, with opportunity for renewal Appointment Start Date: September 2026 Group or Departmental Website: https://med.stanford.edu/neonatology.html(link
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not limited to) identifying effective tutoring practices, running experiments on AI tutors in simulated and real-world environments, fine-tuning AI models to classify qualitative data, and building
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into disease mechanisms, identifying novel biomarkers and developing state-of-the-art predictive models for disease onset and progression. These models are translated into personalized prevention strategies and
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-learning models: We integrate multimodal patient data, including multiomic data and health record information, to develop predictive models for drug response. Furthermore, we work on creating new treatment
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of participatory data (observer bias, taxonomic errors) by coupling them with expert field surveys and Artificial Intelligence (AI) models developed by the Pl@ntNet team (GeoPl@ntNet, Pl@ntBERT). The research
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
including near-future laser scenarios and edge cases. Perform sensitivity analysis and quantify uncertainties/error bars for model predictions. Finalize key figures for results. Month 6 — Finalization