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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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allocation and optimization Joint Communications and Sensing/PNT systems Network virtualization and network slicing MAC techniques/protocols for wireless systems Multi-antenna signal processing Graph signal
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the research team in the area of Swarm Intelligence, Reinforcement Learning and Optimization Techniques. As a Postdoctoral researcher, you will: Lead cutting edge research in Swarm Intelligence and Machine
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or MATLAB, integrating data preprocessing, feature extraction, and classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high
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and antigen expression for vaccine and diagnostic development.•Development and optimization of advanced immunological assays (ADE/ADCC) and yeast-surface display for antibody-antigen interaction studies
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further development and optimization of the Twin-Pipe Pumping (TPP) technology, a protected, extrusion-based 3D printing technology developed at Ghent University which allows for an increased printing speed
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collaboration with ETH Zurich (Switzerland). Within this project, we are seeking a full-time postdoctoral researcher who will play a key role in the development, optimization, and fundamental understanding
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of complex mixtures, experimentally and/or theoretically. Strong background in conceptual process design, simulations tools (e.g. Aspen Plus and Aspen Adsorption), process optimization and TEA. Ability to work
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staff and students. YOUR TASKS You'll be working on a research project focused on data-driven modeling for optimizing production processes in the pharmaceutical sector. Your specific tasks will include
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communication and organization skills are warranted. Given the size of the project, we also expect close collaboration with the wet lab team to optimize workflows and procedures and to aid in data quality control