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information, or quantum algorithms Knowledge of machine learning (e.g., deep learning, optimization, or learning theory) Programming skills in Python and experience with scientific computing (e.g., NumPy, SciPy
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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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and computer scientists PhD applicants must possess a Master's degree in mathematics, theoretical physics, or computer science. Candidates should have an exceptional academic record and a robust
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Science, Machine Learning, Computational Linguistics or a related field, if applicable with PhD previous experience in Natural Language Processing, knowledge Graphs, Machine Learning or Recommender Systems
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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and enthusiastic individual who meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related
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In this project, the selected candidate will join us in conducting research in statistical learning, developing data-driven methods to learn models of large-scale signals and systems from data
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skilled in object-oriented coding (preferably Python) and data analysis; affinity with machine learning and explainable AI techniques, preferably in a geoscience context; good social skills. As a university