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Field
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conducted in collaboration with BioMS and experts in proteomics and peptidomics at Lund University. The duties include: Planning and conducting experiments. Developing and optimizing methods for sample
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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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and its supporting infrastructure • realize on-chip pulsed DNP • design microwave pulse sequences for on-chip DNP using spin physics, numerical simulation tools, and quantum optimal control
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its inverse reconstruction. A key challenge is the data-driven design of the experimental setup: exploring how the choice of measurements and configurations can be optimized to extract the most useful
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utilize low-grade waste heat. The project aims to develop methods for the design, optimization, and operation of integrated systems that maximize both economic performance and environmental benefits
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and optimize lymphoid organoid systems derived from iPSCs and primary human CD34+ cells Perform CRISPR screens targeting genes implicated in lymphocyte development Generate and characterize lymphocyte
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deploying the face de‑identification pipeline on resource‑constrained hardware, optimizing the underlying AI models based on latency, memory, and power constraints, and building demonstrators that showcase
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catalyst structure–performance relationships. The most promising catalysts will then be optimized and evaluated under practically relevant electrolysis conditions, contributing to the development of a
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transition. This ambitious goal will be achieved by developing advanced experimental and computational tools to characterize e-fuel combustion processes, optimize fuel-flexible technologies, and integrate
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to increase resource use efficiency. However, managing and optimizing these new cultivation systems requires crop models that can predict plant growth, crop yield and resource use under highly dynamic