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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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sequencing data and optimise editing conditions Execute pooled functional screens to identify synergistic gene combinations Validate hits with targeted assays and in‑vitro models Contribute to B.Sc./M.Sc
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collaboration that covers all aspects of our research: theory and modeling, sample growth and fabrication, experiments and demonstrations. We have created a dynamic research environment of young and senior
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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Training: Conduct Silvaco TCAD simulations (fabrication processes, device modeling, and circuit-level simulations) . Experimental Work: Participate in cleanroom processes, device fabrication, and electrical
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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, synthetic biology, mathematical modelling and AI/ML and more to design the next generation microbial cell factories. We do this with “the end in mind,” meaning that we have a commercial and industrial
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and kinetic modelling Expression, purification, and characterization of enzymes from fungal and bacterial sources Development and optimization of enzyme assays Structure–function studies of enzymes
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system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models and machine