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Field
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of the PhD student based at CWI in Amsterdam will study integrated hydrogen-electricity markets. In particular techniques from Artificial Intelligence and multi-agent systems for modelling new types of markets
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based on optical trapping or fluorescence microscopy to study RNA polymerase and its response to DNA damage-induced transcription stress; • develop an interdisciplinary skillset by acquiring a
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to turbulence. The effect of external agents, such as free-stream disturbances and wall roughness, will also be taken into account. The methodology of the research is theoretical, based on matched
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application! We are looking for one or two PhD students in Multiagent Automatic Control at the Information Coding Division (ICG), which is based at the Department of Electrical Engineering (ISY). You will be
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that would give you an advantage) Experience in computational modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning, robotics). Experience in annotation software such as
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human analysts and counter huge increases in data volumes. Improved synthetic training utilising machine-based instructors, matched to individual training needs. Improved “Virtual Assistants” for the next
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progress. In this project, you will characterise the microbial, metabolic, and functional composition of FMTs used to treat patients. Your aim will be to identify the key bioactive agents within donor
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that integrates climate resilience, equity, and cost-effectiveness into infrastructure management, aligning with UN SDGs 6 (clean-water) and 11 (sustainable-cities). Objectives Develop physics-based ML models
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PhD Position: Activating Heritage as a Mediator for Dialogue and Belonging in an Era of Polarization
materials as central components for engagement. These tools will be designed and tested through participatory and practice-based methods. Yet, depending on the candidate’s background and evolving project
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. These frameworks will: Identify the causes and responsible agents of liveness breaches, and assess any resulting harm. Support recovery strategies using counterfactual reasoning (“what if?”), guiding programs back