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intelligence, starting 1 April 2027. You will design new sequence-to-expression deep learning models that predict gene expression from DNA sequence in plants, using new experimental datasets generated in the
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-supervised deep learning models that directly incorporate electromagnetic physics. We are looking for a motivated postdoc to lead cutting-edge research that combines methodological development with
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dose and patient burden. The project will include developing new visualisation strategies to aid delineation, as well as developing deep learning methods to enhance photon-counting CT images and better
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We are seeking a postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data
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) to collect, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning
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Experience with programming in Python, R, or similar languages Background in machine learning or deep learning methods Knowledge of genomics, transcriptomics, evolutionary biology, and plant biology is an
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, adapt and use deep learning methods for analyses of omics-based data obtained in the project. Collaborate with international partners and contribute to joint research efforts within the program