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, microscopy, spectroscopy, pore-structure characterization, geochemistry. You are interested in interdisciplinary research at the interface of materials science and microbiology and are motivated to acquire
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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of reinforcement learning (RL) for industrial process control and optimisation. The research focuses on developing RL methods that can support decision-making and control in complex industrial systems while
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for using AI to develop social engineering attempts. This project combines human subject research of learning and decision making, Human-Computer Interaction, and the advancement in AI methods
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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journals. To a limited extent, your appointment will also involve coordinating and supervising exercise sessions, laboratory sessions, and project-based learning activities associated with courses taught by
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. Knowledge of data analysis, optimization, and machine learning techniques is a plus. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP) and software (e.g
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considered a strong asset. Experience with Deep Learning and Artificial Intelligence is considered a plus. Excellent proficiency in the English language is required, as well as good communication skills, both
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enrolled in ULB’s 4-year doctoral programme, in which they acquire many other important career skills. PROFILES Prof. Maes is looking for three highly motivated, intellectually curious, and collaborative
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. Although AI is increasingly being used to support the learning of productive language skills, empirical evidence regarding its effectiveness in foreign language education remains limited. In this project, we