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Offer Description The successful candidate will be expected to work primarily on aspects related to precision predictions for the LHC and parton-shower simulations. The project encompasses the development
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annotating experimental findings from the literature— with advanced machine learning approaches to extend functional annotation of LCRs. • Apply clustering algorithms to group LCRs by similar annotation
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g
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currently deposited at Zenodo. We will re-annotate the proteins to comprehend the underpinnings of fungal ecology and adaptability. The project will integrate information on the evolution of Mortierellomycota