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contributions are: Conduct applied research on atmospheric pressure plasma deposition of functional materials on textiles. Develop and optimize plasma deposition processes for tailoring material properties
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benchmarking and comparative evaluation of gene perturbation models across diverse single-cell datasetsCollaborate closely with Helical-AI on scaling, optimization, and integration of the developed models within
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for new catalyst compositions using DFT-calculations. Qualifications The successful candidate is well-motivated, hardworking, and willing/able to work as part of a team. The candidate should have a PhD
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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biomedical and biological data, including developing and optimizing models to predict disease progression and create realistic patient profiles; Building and optimizing pipelines for pre-processing and
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Development & Maintenance: Drive the development, optimization, and upkeep of the lab's computational tools and analysis pipelines, ensuring that they robustly support and accelerate the team's research
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methods (e.g., PCA, PLS-DA, clustering, neural networks) to enable automated, polymer-specific classification. Optimize workflows for high-throughput imaging and real-world sample variability, minimizing
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Fluid Balance: How do lymphatics fine-tune the equilibrium between immune tolerance and inflammation? How the optimal interstitial fluids? How the resolution of inflammation and edema? Phenotypic
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Disease. The antibodies will be engineered for optimal target engagement. The work involves a close collaboration with PI Dr. Simon Glerup (CSO, Draupnir Bio ApS) and Assoc. Prof. Nathalie Van Den Berge
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: Completed PhD in a relevant discipline. Strong research background and competency in relevant research skills or areas for the post. Examples include: Demonstrated experience in advanced microscopy analysis