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learning objectives work best for sparse, high-dimensional and heterogeneous microbiome data, and turn the selected approaches into robust trainable models. Your work will cover both foundation-model
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objectives work best for sparse, high-dimensional and heterogeneous microbiome data, and turn the selected approaches into robust trainable models. Your work will cover both foundation-model pretraining and
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attention and cognitive processing. You will work in a multidisciplinary team using cutting-edge experimental methods, including eye tracking, pupillometry and behavioural paradigms, to advance our
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engineering, molecular cloning, genomics, and computational data analysis workflows. In addition you: have obtained, or are on track to obtain, a PhD with first or co-author publications/preprints in
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workflows, Data normalization and quality control strategies, Strong experience in processing and interpreting complex MS datasets; Track record of peer-reviewed publications in relevant fields; Excellent