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representations from massive amounts of raw data, typically using large-scale models that incorporate only minimal prior domain knowledge. Such representations can support a wide range of downstream tasks. However
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fundamentals of networking. Objectives: To achieve on-device spectrum sensing using on-board sensors of mobile BSs, empowered by embedded deep learning algorithms; to propose an analytical model for the cell
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improve the efficiency, maneuverability, and noise performance of drones and other multirotor aircraft, but their deployment requires more advanced modeling and control methods. The PhD will focus
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neuroinflammation. The PhD student will focus on characterizing immune cell responses in food allergy models and their impact on brain immunity. In close collaboration with experts in food allergy, neuroimmunology
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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environmental factors. To address these challenges, we have developed a preclinical rat model to monitor structural and functional connectivity as well as neuroinflammation non-invasively over several weeks using
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partially filled containers – is a major challenge in aerospace, transport, and energy systems, where it can compromise stability and safety. The PhD will focus on developing low-order models of sloshing
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This interdisciplinary project uses advanced stem cell-based embryo models, called blastoids (developed in the laboratory of Prof. Vincent Pasque), which mimic the early human blastocyst stage. Your work will focus
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of the anti-MMP9 Nano-antibodies in a mouse model of delayed healing and a 3 weeks training at Nordic Bioscience( Herlev, Denmark) in the team of Dr. Signe Holm Nielsen to establish a Nano-antibodies based
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model-based control tailored to systems like NudgeFlow. The framework decomposes the global control problem into smaller local problems for each zone or room, enabling independent optimization with local