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of chemical research and curious about how automation and data-driven approaches are reshaping laboratories? Do you want to contribute to an ambitious, interdisciplinary research project at the interface
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captured either by globally over refining the mesh, which is computationally expensive and environmentally inefficient, or by running multiple successive simulations to iteratively adjust the mesh. Both
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. This project aims to develop novel polymer-based nanoparticles for the non-viral delivery of mRNA vaccines directly to mucosal tissues, such as the respiratory tract. By leveraging high-throughput automated
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requirements. The resulting tools will support automated mesh generation and adaptation, reduce manual tuning and improve the reliability of simulations involving geometry driven behaviour across multiple
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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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multiple scales, while Multiobjective optimization strategies ensure improvements in efficiency across various biomanufacturing scales. Expected results: 1) Development of innovative bio-based production
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Applying machine learning-guided directed evolution to improve multiple enzyme properties Upscaling selected biotransformation reactions in collaboration with academic partners Responsibilities and
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Delft about the new insights we gather. We will focus on open science, sharing our results to help developers across the world. Automated Code Refactoring The focus is on exploring and evaluating
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. Research areas: Computer science, Simulation-based automated synthesis and verification of human physiology models, Artificial intelligence, Machine learning Secondments: The candidate is expected to spend
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ensure the integrity, synchronization, and seamless integration of this vital multimodal data. You will be at the forefront of developing the automated, robust, and scalable infrastructure required