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Field
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the use of instrumental analytical methods such as HPLC-MS, GC-MS knowledge in the preparation of plant tissues for the extraction of natural substances experience in the application of molecular biological
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specifically on tumour metabolic targets. You will develop and support the INSTINCT-MB. programme through the provision of experimental, analytical and bioinformatic expertise. This will include the development
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of the research group. Desired qualifications and skills: A relevant background in aquatic biology, animal physiology or a related field. Good skills for laboratory-based analytical tools. Practical experience
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, Machine Learning, AI, and related areas including Optimization, Mathematics and Physics are welcome to apply. The candidate is expected to conduct independent research and should have strong analytical
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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and the data analyses of mass spectrometry data. Attending a total of six months secondments to learn novel algorithms form improving fecal MP samples' taxonomic and functional characterization
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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. Qualifications The applicants should possess: An excellent or very-good university degree in economics, business studies, agricultural sciences with a focus in economics, or related disciplines Strong analytical
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especially to the ‘EA FC’ game, formerly known as the ‘FIFA game’. Its popularity positions the game as an informal learning site for ideas about societal issues. This includes ideas about race and ethnicity
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biosynthetic machinery Applying novel GAG analytical technologies to investigate how changes in GAG structure and organisation drive and/or respond to shifting developmental stages. Working Environment: Based