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Field
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Are you interested in advanced rheological characterization, microstructure analysis, and modelling of complex food systems? In this postdoctoral project, you will combine fundamental science with
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and communication across multiple modalities, such as text, pictures, audio, and video? Join the large scale HAICu project to help unlock the potential of cultural digital archives through multimodal
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data-generation and training workflows, including sampling, active learning, transfer learning, validation, and uncertainty or robustness analysis, to reduce the cost of generating excited-state training
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tube of blood. The successful candidate will lead the development of new computational and AI-driven concepts for cfRNA analysis, working at the interface of wet lab, dry lab, and the clinic. We
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and PBWT-based algorithms (RaPID, RAFFI, FiMAP, ROH analysis, local ancestry inference), now extending into GBWT/RLBWT-based pangenome indexing, efficient pangenome graph construction and query, cross
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well as public databases. Expertise in the data analysis of one of the modalities that we will use in this project (single cell transcriptomics, ATACseq, proteomics or long-read DNA or RNA sequencing) will
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functional microscopy, multi-modal and meso-scale imaging, clinical imaging innovations ranging from histopathology to whole-body imaging, tracer / indicator development, wearables and quantification
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, diffusion MRI, EEG, and other neural signal modalities. Build scalable training and evaluation pipelines for large neuroimaging datasets. Collaborate closely with researchers across AI, neuroscience
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collaboratively in a multidisciplinary research setting. * Strong written and verbal communication skills. Preferred Qualifications * Experience with single-cell data analysis and multi-modal data integration
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methods of basic biomolecular research with analysis methods of bioinformatics and analytical high-performance technologies to investigate the complex interplay between the human organism and food