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and signaling biology in the context of the hallmarks of cancer, and the molecular biology connecting DNA mutation and methylation to RNA and protein function. Familiarity with liquid biopsy modalities
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electric fields), and solvent‑shifting approaches (pH and salt shifting). Evaluate and integrate green processing technologies, including deep eutectic solvents, for protein extraction, off‑taste reduction
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characteristics, brittleness vs. plasticity, and frictional/lubrication properties. Generate mechanistic insights linking textural dynamics to sensory perception. Integrate multi-modal datasets from different
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the broader areas of multi-modal learning analytics, AI in education, and (computer-supported) collaborative learning. The successful candidate will join the interdisciplinary DFG-funded project AEyeCoL
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, serum, urine, and other biological samples. Integrate multi-modal datasets to uncover mechanisms underlying kidney disease progression and treatment response. Apply advanced microscopy image analysis and
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of the linguistic component of the SIGNFLOW project, including the linguistic analysis of Portuguese Sign Language (LGP) and American Sign Language (ASL) as the project’s reference languages and, where applicable
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, cartographic analysis, and fieldwork; preparation of synthesis drawings based on completed and ongoing surveys, as well as thematic cartography to contextualise the heritage of the Fuzeta/Moncarapacho region
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analysis. This position offers a unique opportunity to work at the forefront of biomedical data science and contribute to discoveries that advance the understanding of cancer, cardiometabolic diseases
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and multi-modal biological datasets. The successful candidate will be embedded across both institutes, benefiting from joint supervision, collaborative meetings and access to world-leading expertise
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development and application of data processing workflows, working closely with existing team members across optical, infrasound and RF sensing to contribute to a genuinely multi-modal picture of atmospheric