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Healthcare Monitoring We welcome applications from those with expertise in or across these disciplines: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale
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be funded within the project titled ”A Probabilistic Inverse Model for Identifying the Source of Atmospheric Contamination on a Continental Scale” funding under the prestigious SONATA 20 competition
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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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Functions Cleaning and organizing datasets Performing exploratory data analysis Preparing features for modeling The student will implement and evaluate statistical and ML models for various applications
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. In particular, we will develop a general deep learning framework that will advance and speed up AI model development in physics analysis. A particular focus of the project will be on: 1) Graph Neural
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computational models generate hypotheses and, with the help of partner labs, validate them in controlled systems. The end goal is a mechanistic and clinically relevant map of how CIN shapes cancer behavior and
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systems whose near-field radiative capabilities allow for focusing and localizing EM energy. Predictive models, based on EM simulations and extensive measurement campaigns, will also be developed
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candidate will contribute to the development of diagnostic methodologies and lifetime and aging models to assess the degradation state and reliability of these insulating systems. Where to apply Website https
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for part-time employment. Starting date: 14.01.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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, such as semi-Markov models linked to disease transmission models. The framework builds on existing models but these must be adapted to account for differences in epidemiology and disease burden between