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approach, combining machine-learning–enhanced text-as-data analysis with qualitative discourse analysis. The project aims to produce a set of high-quality scholarly outputs, including peer-reviewed journal
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European cities. The project explicitly embraces a broad AI perspective, including (but not limited to): machine learning and statistical learning computer vision and sensor-based data analysis natural
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fully funded PhD position in machine learning, AI, and data science for public health within the Electronics & Informatics Department (ETRO) of Vrije Universiteit Brussel (VUB). The PhD is embedded in
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• Interest/knowledge of statistics, computer science and/or machine learning • Interest in biology or molecular biology, microbial ecology • Proficiency in programming languages such as Python
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multiple symbolic fields, including official political discourse, academic debates and selected cultural outputs. Methodologically, it adopts a mixed-methods approach, combining machine-learning–enhanced
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, incorporating transient tribological changes. Creating machine-learning-based surrogate models to enable rapid efficiency and lifetime predictions under realistic operating conditions. Validating the developed
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the research team in the area of Swarm Intelligence, Reinforcement Learning and Optimization Techniques. As a Postdoctoral researcher, you will: Lead cutting edge research in Swarm Intelligence and Machine
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, collaborative science Experience with tools for qualitative and quantitative analysis; experience and practice with machine learning and Artificial Intelligence are also considered assets Language requirements
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of competitive research proposals. You should have experience in the following areas: Applied Machine Learning for Autonomous Systems: Experience developing and deploying ML models for perception, prediction
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with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g., ROS, Gazebo, Mujoco, IsaacSim