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to this third-cycle studies which corresponds to four years. Position description We are seeking a PhD student to join our research team specializing in the analysis and modeling of multiphase flows. The research
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: Experience in screening molecules in cell models Bioinformatics with a focus on pathway enrichment analyses (e.g., Metascape) Understanding of the pathophysiology of sepsis Knowledge of drug development and
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biology methods and bioinformatics methods advanced cell culture, RT-PCR, immunostaining, microscopy, flow cytometry, analysis of single cell and bulk omics sequencing data as well as mathematic modeling
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systems conduct experimental and clinical imaging studies on biological samples and patients analyze spectral data and develop classification models compare imaging findings with histopathology write
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lab set-ups, using real and model waste feedstocks. Some of the experimental studies will be conducted in lab-scale setups at the Department of Chemistry but given TNO’s unique infrastructure we will
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directions include: Quantitative genetics and phylogenetics: incorporating developmental constraints into evolutionary models and exploring how they shape patterns of variation. Modeling development from data
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essential tool for training and testing of AI models and control systems for robots and autonomous vehicles. In a digital environment, large amounts of annotated training data can be created safely and easily
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the transition towards more regenerative and adaptable furniture systems. Results will be shared through publications, demonstrator projects, and educational models, strengthening the link between research
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essential tool for training and testing of AI models and control systems for robots and autonomous vehicles. In a digital environment, large amounts of annotated training data can be created safely and easily
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this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural