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Join our exciting research project as PhD candidate at Utrecht University and become part of the research collaboration AI4Oversight lab which is part of theInnovation Center for Artificial Intelligence (ICAI).
The Dutch government inspectorates play a critical role in safeguarding public interests such as food safety, a clean environment, and quality of education. To ensure effective oversight with a limited capacity at strategic and operational level, inspectorates need to work in a data-driven way and embed AI technology in their primary processes.
By joining the ICAI lab AI4Oversight, you join a community that collaborates to address AI challenges specific to the inspection domain leading to scientifically attested methods. The AI4Oversight lab connects the Human Environment and Transport Inspectorate (ILT), the Netherlands Labour Authority (NLA), the Inspectorate of Education (IvhO), Netherlands Food and Consumer Product Safety Authority (NVWA), Netherlands Organisation for Applied Scientific Research (TNO), Utrecht University and Leiden University. Collaboration between these organisations is seen as an essential element of our lab. Working together enables not only to develop new knowledge, but also to use each other’s expertise, to experiment together, to learn from each other and to bring theory to practice.
The execution of the research will be highly participatory. You will spend time at the offices of funding partners and have the opportunity to dive into the practical challenges and way of working of the partners. You will work together with data scientists of the inspectorates, who will contribute with practical experiences and use cases. Within the AI4Oversight Lab you will be part of a collaborative environment with at least five other PhD candidates, where you regularly engage in knowledge exchanges to strengthen cross-disciplinary collaboration.
Your work aims to advance risk classification beyond binary labels by learning interdependencies between inspection items using probabilistic graphical models like Bayesian networks. These models aim to support interactive inspections by prioritizing items dynamically, combining data-driven learning with expert knowledge to handle incomplete information effectively.
Your key responsibilities will be to:
You are equipped with a critical mindset and motivated to use your experience in education and research to make a valuable contribution to research in the field of artificial intelligence and machine learning. Next to that you have the following qualifications:
A background check may be part of the selection procedure.
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