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) and related to the major multi-national initiative “GOE-DEEP” supported by the International Continental Scientific Drilling Program (ICDP) and aiming to study the climate at the time of the first rise
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related field Solid understanding of machine learning, especially deep learning and transformer models Practical experience with Python and ML frameworks (e.g., PyTorch, HuggingFace, NumPy, sklearn) Basic
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Science or equivalent degree) in biological sciences or bioinformatics Experience with population genomic and population genetic research Deep understanding of Evolutionary Biology Experience or interest in
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host chromatin pathways (DFG Research Unit DEEP-DV, FOR5200). The group uses experimental infection systems, an array of high-throughput sequencing methods, and single-molecule live-cell imaging
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learning and deep learning Excellent programming skills in Python Practical experience with PyTorch (preferred) and/or with TensorFlow, scikit-learn, and GitHub Experience with scientific experimentation and
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages