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, compression, learning, and inference for classical and quantum data exchanged through classical and quantum networks. The objective of the PhD study is to explore and address research and design challenges
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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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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
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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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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
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HPLC-MS, GC-MS experience in the extraction of natural compounds from complex matrices knowledge of the isolation and structural elucidation of natural products experience in the statistical analysis
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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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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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starlight. In ICE-EEVOLVE , we seek to unravel how chiral organic molecules, trapped in amorphous ice matrices, evolve from molecular clouds through star-forming regions to planetary systems. Laboratory