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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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modelling methods to design resistance-proof antibiotics. You will join an interdisciplinary team, integrating machine learning, medicinal chemistry and microbiology. You will work with Asst. Prof. Eli N
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and have synergiccollaborationeffects. Weexpect a motivatedearlycareer researcher with stronginterest and experience with GIS/earth observation/climateprojection data as well as machine learning models
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quality modelling, with focus on Knowledge-Guided Machine Learning. The position is a rewarding opportunity to be integrated in an excellent freshwater group. The department’s research and advisory
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Postdoctoral Researcher with a strong computer science background and demonstrated expertise in deep learning and generative model development to lead the AI component of this initiative. Responsibilities and
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computational datasets of disordered materials based on density functional theory calculations and training machine learning models to accelerate the predictions. This work will involve collaboration with Assoc
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novel discoveries for the benefit of the human health. Responsibilities and qualifications Your overall focus will be to strengthen the machine learning and computational modeling of our project, in close
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Python Documented experience with NLP and computational text analysis Experience working with language modelling, semantic analysis, or related machine learning approaches Requirements Documented
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in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and practical experience in deep learning Has hands
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learning–based), advanced mesh generation techniques for simulation, and experience with biomedical simulation, both virtual and physical. Experience with laboratory and clinical validation of models is