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PhD candidate for the project "Modulation of inflammatory Dendritic cell subsets in atherosclerosis"
, bright, well-motivated and ambitious scientist: With a recent Master degree in Bio-Pharmaceutical/Bio-medical sciences, immunology, molecular biology, or a related field, Strong background in immunology
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, agency and representation in EU institutions? And how are EU and national institutions taking into account this minority in its policy-making? The main scientific objective is to answer these under
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their historical contexts from prehistory to the present. Our faculty is home to more than 6,000 students and 800 staff members. For more information see: https://www.universiteitleiden.nl/en/humanities . Diversity
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3D X-ray inspection techniques for industrial quality control. The project is foundational in nature, while at the same time exploring applications in collaboration with industry partners. The main
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administration and governance. You will teach in our Bachelor and Master programmes in Public Administration. Alongside teaching, you have or are looking to develop your own research agenda that is theoretically
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and will be supervised by: Main supervisor: Dr. Rik Mom, Leiden University https://www.universiteitleiden.nl/en/research/research-projects/science/operando-research-in-electrochemistry Co-supervisor: Dr
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that reflect real human diversity. What you will do In this project, you will work with human induced pluripotent stem cells (iPSCs) and primary cells derived from donors with different (genetic) backgrounds. By
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at the Bachelor and Master of Science level in Artificial Intelligence, Computer Science, ICT in Business, Media Technology, and Bioinformatics. According to an independent research visitation, we are one
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, machine learning, and foundation models. Research Focus The project investigates how visual foundation models can efficiently adapt beyond their original training experience and acquire expert-level
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partners. The main goal of your PhD project will be to develop combined X-ray and visible-light imaging technologies and machine learning methods for high-throughput inspection tasks. The work will include