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in bioinformatics, molecular biology, computer science, or a related field; Strong bioinformatics skills, including programming in a Linux environment (Bash, R, Python) and familiarity with commonly
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with experience applying these models to practical applications for real-world challenges. Good programming skills, Python is mandatory, other languages (e.g., Java, C++) will be considered a plus to
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technologies in NLP, LLMs, and RAG. Programming skills in Python, including experience with machine learning libraries (e.g., PyTorch, Scikit-learn, Hugging Face Transformers) and development tools (e.g., Co
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technologies in NLP, LLMs, and RAG. Programming skills in Python, including experience with machine learning libraries (e.g., PyTorch, Scikit-learn, Hugging Face Transformers) and development tools (e.g., Co
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technologies in NLP, LLMs, and RAG. Programming skills in Python, including experience with machine learning libraries (e.g., PyTorch, Scikit-learn, Hugging Face Transformers) and development tools (e.g., Co
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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-analysis tools and publicly available genetic-resource databases. Knowledge of R, Python or related programming tools. Additional scientific activity, such as publications, conference presentations
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interdisciplinary research; familiarity with programming (e.g., Python) will be considered an advantage; previous experience in singlemolecule methods is not required, as full training will be provided