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Field
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AI4Science project, specifically focusing on the intersection of advanced machine learning and sustainable catalysis discovery. The primary incentive of this Postdoctoral Fellowship is the chance to contribute
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parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible
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field with a strong academic background in Machine learning, Natural Language Processing, and/or Software Development. Applicants with an experience in one or more of the above academic fields are
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, Telecommunication Engineering, or a related field with a strong academic background in Machine learning, Natural Language Processing, and/or Software Development. Applicants with an experience in one or more of the
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with a strong academic background in Machine learning, Natural Language Processing, and/or Software Development. Applicants with an experience in one or more of the above academic fields are encouraged
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innovative research in three major categories: AI Fundamentals: machine learning, deep learning, data science; AI Core Applications: computer vision, natural language processing, speech processing, robot
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Publications relevant to the position: J. M. Montes-Lopez, P. Serrano, M. Gramaglia, A. Banchs, “DiWi: A Transformer-Based Digital Twin for Wireless Mobility”, Elsevier Computer Networks M. Milani, V. Vomhoff, D
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Personal Competences: Demonstrated competitive ability in using DFT simulations, and machine learning techniques and DFT. Demonstrated strong coding skills and a passion for UX/UI design. Summary
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research databases (0 to 10 points). ▫ Use of Machine Learning and Big Data for biomedical research (0 to 5 points). ▫ English language proficiency (0 to 10 points). Specific Requirements Required
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specialised libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Knowledge of machine and deep learning methods and techniques, including training, adjustment and development of language models and LLMs