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Field
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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algorithm that allows accurate simulation of fluid transport processes in porous media coupled with chemical reactions (e.g. dissolution and precipitation). The algorithm will be validated firstly against
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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lead to a PhD position at SPMS in NTU Singapore (based on performance). Key Responsibilities: Contribute to the development of algorithms Develop codebase in Python and perform large-scale simulations
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use in real manufacturing workflows. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Project
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: Develops advanced Agro-geoinformatic algorithms for monitoring and predicting the conditions of crop, pasture, and their environment with advanced remote sensing and geospatial technologies; Develops and
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on real-life data from the maritime industry. As part of the PhD, you will be following advanced courses to extend your skills, implement and test algorithms, and learn to write scientific papers. As part
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algorithms for resource-efficient hydroponics and evidence-based frameworks for integrating green space exposure for improved student wellbeing. This project will potentially enhance urban food resilience
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(IQU, Spain) DC2: Design of novel distributed algorithms for CSI estimation (KUL, Belgium) DC3: Synchronization methods for CF networks (KUL, Belgium) DC4: Design of holistic neuromorphic solutions in 6G