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methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based methods. As part of a
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of December 2025, or as soon as possible thereafter. The duration of the position is two years. The successful candidate will be hosted by the Visual Analysis and Perception Lab. You can read more about VAP LAB
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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modalities. Therefore, the ideal candidate will be expected to conduct homologous overexpression, gene silencing, sequence analysis and contained phenotyping. Education A PhD in Molecular Biology, Genetics
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in relevant research fields Desirable criteria Multi-modal image analysis expertise Real-time detection and segmentation methods for clinical applications Surgical video analysis, e.g. workflow
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. The successful applicant will integrate multi-modal live imaging and omics data using AI-based pipelines to identify and refine early disease phenotypes, laying the groundwork for therapeutic intervention
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, Science, 2022), especially in the context of drug-host microbiome interactions, and the development of dedicated tools for the novel data modality generated by Record-seq. Job description We are looking
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on the analysis of different characteristics of the skin and body fluids, such as sweat and blood. Many of these methods are challenged with quality (accuracy/precision of measurement), power consumption, usability
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. Particular emphasis to be placed on developing and applying emergent system identification and modal decomposition techniques as well as statistical analysis of large datasets. Candidate will disseminate
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, multi-modal, and longitudinal data with detailed information about mothers, their pregnancy, and the long-term development of their children. You will be part of a dynamic and highly motivated research