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Field
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based
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Ability to curate and integrate large scale biomedical data Experience with workflow management and HPC environments Interest in RNA biology, alternative splicing and computational method development Strong
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within a research group with strong expertise in vascular access, large animal models and clinical translation. At the same time, you will be scientifically embedded in research groups at TU Delft focusing
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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at the interface of neuroscience, technology, rehabilitation and clinical practice. Additional Information Benefits We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your
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. Opportunities for joining study programs using big data analytics will be provided. For further enquiries about the duties of the post, please contact Prof. Sherry Chan ([email protected] ). A highly competitive
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an opportunity, several challenges need to be faced before approaching a clinical study. In particular strategies to irradiate large clinical targets need to be defined and dosimetric approaches able to verify
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regarding the added value of spectral imaging and super-high-resolution (SHR) imaging. The outcomes of this feasibility phase will guide the development of future large-scale clinical studies. The project
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omics-based approaches. Interest in spatial biology, single-cell/spatial omics technologies and the integration of tissue-based, molecular and clinical data. Basic computational and data-analysis skills
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clinical, sequencing data, and circulating biomarkers, the project aims to: Develop and validate advanced machine learning models for the stratification and diagnosis of ANOCA; Provide cell-type–resolved and