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on developing a new multi-disorder prediction approach that integrates different sources of information. You work with analytical model development, extensive simulation studies and analysis of existing large
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, particularly sialic acids, influence and suppress immune responses against peritoneal metastases; test in patient material and in vivo models whether sialic acid inhibition reactivates the immune system; analyze
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» Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Netherlands Application Deadline 27 Feb 2026 - 12:30 (Europe/Amsterdam) Type of Contract To be defined Job Status
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statistical models to predict treatment response; optimizing individualized rTMS targeting using neuronavigation and computational modeling; designing and conducting n-of-1 trials embedded in routine clinical
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environments. The researcher will work on modelling relevant propagation effects, designing localization strategies robust to urban acoustics, and validating these techniques using state-of-the-art experimental
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OCT system must be optimized in terms of noise and detection efficiency. You will do this based on experiments and physical modeling of SC, detector, and system performance. Second, the OCT system must
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involving Fluid Dynamics. Demonstrable affinity with studying the physics of the ocean and preferably with polar oceanography. Experience with numerical modelling. Excellent ability to communicate in both
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like to know more about the different phases within the PhD trajectory? You can read more about this on this page . As part of this project, you will use innovative neuronal cell models and advanced
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architecture, preferably with a focus on structures. Know the basics of (structural) glass and of Dutch masonry (heritage) buildings. Have a solid understanding of computational/numerical modelling and
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work on the following: Design of an efficient foundation model (FM) for generalization across patient anatomies, pathologies, and coil arrangements to infer optimal sensor settings from partial data