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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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the 1970s and 1980s; and its longer-term methodological and conceptual impact. The historical research is intended not simply as a parallel study, but as a source of conceptual insight for the contemporary
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disciplines; the prominence of chaos theory in the 1970s and 1980s; and its longer-term methodological and conceptual impact. The historical research is intended not simply as a parallel study, but as a source
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bioinformatic approaches. Working in a highly collaborative environment together with the Artegiani-Hendriks group in the Máxima, you will uncover the developmental origins, molecular mechanisms and sex-specific
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machine learning; acoustic signal processing enhanced by ML; human-in-the-loop/active-learning methods; representation learning; analysing sound sequences and vocal interactions; category discovery; Perma
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predictions into biological insights and identify ancient genetic traits that may contribute to future climate-resilient agriculture. Within Wageningen University, you will be embedded in the Bioinformatics
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-disciplinary domain. Specific research topics to apply to bioacoustics might include: low-footprint machine learning; acoustic signal processing enhanced by ML; human-in-the-loop/active-learning methods
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well as lineage-specific variants. Your duties Using your experience and enthusiasm for mass spec, in-situ hybridisation and bioinformatics, you will start by refining the mass spec imaging technique for work
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related field; Experience with culturing aquatic microorganisms, physiological assays and fieldwork in marine ecosystems; Experience with omics-techniques and bioinformatic/computational analyses are a plus
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related field; Experience with culturing aquatic microorganisms, physiological assays and fieldwork in marine ecosystems; Experience with omics-techniques and bioinformatic/computational analyses are a plus