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Field
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. In this PhD-project, you will propose and evaluate new AI methodology to ensure that organoid data can be used to optimally predict relevant patient outcomes. You will use a real-world case study on
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organisms have been lacking. You will build on our recently developed single-cell ribosome profiling methods for C. elegans and further optimize them to apply to the early embryo. With this approach, you will
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industrial contexts; investigate optimal levels within the product structure for deploying AM in repair and spare parts support; integrate forward and reverse flows in manufacturing and maintenance
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and thus go beyond relatively straightforward safety and progress properties, system requirements related to the timing of events and the results from performance optimization need to be included in
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), live-cell imaging, and transcriptomic analyses. Laboratory work will involve optimizing protocols to convert patient-derived skin cells into neurons (iNeurons). New AONs will be screened through reporter
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, and engineering to optimize and upscale and biodegradable structures that temporarily mimic key emergent traits using industrial-scale additive manufacturing (i.e., 3D-printing) techniques
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optimization algorithms, you will design structures that deliberately harness modal couplings to exhibit tailored nonlinear behaviour, with direct applications in ultrasensitive resonant sensing. Together
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the second direction, you will explore the geometric design of nonlinear systems. Using nonlinear reduced order modelling (ROM) integrated with optimization algorithms, you will design structures
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. Specifically, the team will test a new framework that combines methods from ecology, industrial design, and engineering to optimize and upscale and biodegradable structures that temporarily mimic key emergent
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requirements in conflict zones and other industrial contexts; investigate optimal levels within the product structure for deploying AM in repair and spare parts support; integrate forward and reverse flows in