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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
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to the development of novel tools for cancer risk assessment with real potential impact on healthcare. Qualifications Requirements A doctoral degree or an equivalent foreign degree in computer science, statistics
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cybersecurity tool development, virtualised networking test beds, application of statistical or AI methods for load monitoring and performance tuning, and anomaly detection are prioritized. Excellent
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
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relevant medical science. Strong skills in data management and the ability to work with large datasets. Strong statistical analysis skills. Strong programming skills in a relevant statistical software
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preparation for proteomic analysis, preferably at single cell level. Experience in performance, evaluation and maintenance of nLC-MS/MS systems. In-depth knowledge and experience in statistical and
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international peer-reviewed journals. Written and oral communication skills in English at an academic level are required, as are relevant skills in statistical analysis. The candidate is expected to be able and
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level are required, as are relevant skills in statistical analysis. The candidate is expected to be able and willing to take initiative and to work both independently and in a team. Inter- and
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assets. Data processing skills using statistical computing and graphics, e.g. R, MATLAB, are of high merit too. Cross-disciplinary experience in presenting and communicating scientific results, especially
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statistical skills (R preferred). Practical experience working with farm animals, ideally pigs. Ability to independently plan and conduct research, manage fieldwork, and collaborate within multidisciplinary