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Field
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). Information Key Responsibilities: Develop a generalizable and explainable (gray-box) model for adaptive patient monitoring. Utilize a mixed approach combining real and synthetic data for algorithm development
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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Application deadline: 15 March 2026 Apply now This position focuses on fundamental open problems in algorithm design and computational complexity. The main theme will be a unifying theory of algorithmic power
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); Optical system analysis and simulation; Developing AI/ML algorithms; Perceptual and objective visual quality assessment for holograms. For this function, our Brussels Humanities, Sciences & Engineering
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microscopy is often limited by slow acquisition speeds and large volumes of redundant data, restricting its applicability in real-world scenarios. The goal of this PhD project is to develop a snapshot spatio
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, abandonment, comments, peer interaction) Formalization of algorithms for orchestrating educational AI agents : Train RL and LLM agents and study multi-objective optimization (mastery, well-being stability) Work
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Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains
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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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candidate will develop and conduct empirical research examining human interaction with algorithmic decision-support systems in clinical contexts. The precise focus of the project will be refined
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analyse and develop new, well-founded methods and learning algorithms that extend the boundaries of existing techniques - for example, with respect to expressivity, generalization, interpretability