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head-on. We will reinvent generative cooperative vision and semantic compression methods so fleets of intelligent machines can perceive the world robustly, efficiently, and in a trustworthy manner—even
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obesity. The matrices will support long-term cell culture in microfluidic systems to capture early tumorigenesis and will be functionalized with relevant tumor-promoting factors (e.g., pollutants, glucose
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or 3-dimensional spaces, enabling insights about the underlying structure and distribution of the data. However, due to the heavy data compression into a space with only 2 or 3 degrees of freedom
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should demonstrate expertise in both qualitative and quantitative carbohydrate analysis, particularly within complex food matrices. The successful candidate will preferably have experience engaging
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documentation are not in a Scandinavian language or English, the applicant must upload certified translations. If attachments exceed 25 MB, they must be compressed or provided as links. Applicants with foreign
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(LC-MS), you will explore the occurrence, diversity, and transformation of PAs in food matrices. Your research will involve: developing sensitive and selective LC-MS methods using deuterated internal
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is, however, the transportation and storage. Current methods rely on liquid compressed hydrogen, which requires high pressures or low temperatures. This project will computationally explore catalyst
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) light source that can suppress spurious nonlinearities in direct drive fusion target compression. As part of the IGNYTe team, you will develop a laser comprised of a shapeable incoherent fibre front
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pressure shock waves which induce compressive residual stresses in the structure, thereby improving the surface hardness and the resistance to fatigue cracking and to corrosion. LSP is more effective than
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features enhance binding to the receptors. This information will be carried forward to human tasting panels. You will also investigate how other components of food matrices inhibit binding. You will thus