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drugs in a variety of different matrices. We enjoy the new challenging projects regarding food and feed safety, for example in relation to the circular economy, climate change and new protein sources
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functional priors from billions of years of evolution; how to compress measurements with controlled mixtures of molecules; and how to align models of laboratory experiments with observational human biology
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, model compression, and custom hardware acceleration to advance the state of the art in edge LLM. This position offers a unique opportunity to be at the forefront of technological advancements that promise
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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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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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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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(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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(EBSD, EDX, SAXS, TEM) and ex situ/in situ nanoindentation-based techniques (micro-tensile/compression). The insights gained from this investigation will be crucial for optimizing existing Mg-RE alloys