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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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on behavioral lab experiments but may also include other empirical approaches depending on how the project develops. During the PhD, you will: Learn how to combine theory-driven empirical sociology with
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Qualification portfolio. You will be part of the WECARE research team, led by Dr Sonja Marzi (PI). Your supervisors will be Dr Sonja Marzi, Dr Tine Davids and Dr Edwin de Jong. Would you like to learn more about
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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling
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approach that will be used is Challenge-Based Learning (CBL) in which multi-disciplinary teams of students learn by conducting research and design projects on a societal problem in collaboration with
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industrial partners. You are a naturally curious person who is eager to learn more and has a strong interest in research. Excellent written and oral communication skills in English are a prerequisite. We
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generate new knowledge and critically assess approaches that integrate scientific insights with artistic research to address climate justice. JUST ART PhDs will study and develop concrete cases to learn how
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for transport You hold, or will shortly acquire, a Master’s degree in a related discipline. Ability and passion for operating at the intersection of different fields, particularly transport engineering and
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interested in using AI to unravel the mysteries of the brain? Do you want to perform cutting-edge NeuroAI research and leverage deep learning to understand human vision? Then check out the vacancy below and