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methodologies for analysing RNA modification readouts from large transcriptomic datasets. This position will focus on developing probabilistic deep learning frameworks to identify molecular determinants
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epitopes and c) lack of Deep Learning (DL) tools tailored specifically to the particularities of TCR interactions. As part of the Deep Immune Receptor Modeling (DIRM) grant from the NNF Data Science
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or prior experience with multilingual or low-resource NLP Programming skills in Python and proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and their application in high-performance
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or prior experience with multilingual or low-resource NLP Programming skills in Python and proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and their application in high-performance
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for robotics Robotics simulation and synthetic data generation Robot manipulation Deep learning-based planning You preferably have experience working in a biology or chemistry laboratory environment
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primary role will be to contribute to project tasks that demand a deep understanding of electrical or thermal energy system operation and dynamics, modelling approaches, and optimization methods. To excel
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ATAC-seq, single-cell RNA-seq, spatial gene expression, and whole-genome sequencing (with long reads) data. The candidate will get the opportunity to explore new analysis methods using deep learning