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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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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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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