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to hybrid AI–mechanistic models for predicting micropollutant removal efficiency. Main supervisor is Professor Jan H. Christensen (jch@plen.ku.dk ), phone: +4535332456). The second PhD will focus on large
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the conditions of crop, pasture, and their environment with advanced remote sensing and geospatial technologies; Develops and refines algorithms and workflows for crop and pasture monitoring, modeling, prediction
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 26 days ago
learning models for antimicrobial activity prediction (e.g., Weka); - Strong communication skills; - Fluency in English (written and spoken). The candidate must demonstrate interest in microbiology and
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hydrogenation, dehydrogenation, and hydrogen transfer reactions. Detailed characterization and kinetic studies will be performed to test computational predictions and microkinetic models, and to refine machine
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identification, i.e. learning of models from measured data, and iii) real-time control, e.g. using the model predictive approach. We are working on several projects with industrial partners across the energy
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highly motivated PhD student to develop advanced fracture models for predicting material degradation and failure in additively manufactured steel in nuclear reactor water environments. The project focuses
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intelligent surfaces Main supervisor: Prof. Viktar Asadchy[AALTO] Co-supervisors/mentors: Dr. Victoria Tormo [INDRA] and Dr. Barthès [3DEUS] Objectives To establish an analytical modeling approach
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to strategic initiatives through advanced analysis, forecasting, and predictive modeling that enhance access to, confidence in, and effective use of institutional data across the College. Kellogg Community
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for the next 2.5 years at the interlink of prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for
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work on research projects employing latent variable modeling and risk prediction methods to better understand substance use related morbidity and mortality outcomes (e.g., overdose, hospitalization