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and Artificial Intelligence (CDAI) Support Unit. The successful candidate will be responsible for defining the institute's computational and data-driven strategy, integrating Machine Learning (ML
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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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to provide internal operational guidance and advanced tools that accelerate the adoption and development of machine learning (ML) methods across the centre. Its mission is supported by a set of strategic lines
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characterize new reticular materials (COFs), and use them as precursor for the generation of new polymers via Clip-off chemistry. The candidate will acquire a great experience in supramolecular, reticular and
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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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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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well as regional efforts such as the Quantum Catalan Academy (https://cataloniaquantum.eu/ ) and the Master in Quantum Science and Technology (https://quantummasterbarcelona.eu/ ). These initiatives contribute to a
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to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and electronic
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. Knowledge and Professional Experience: Interest of learning (S)TEM, EM related spectroscopies and in-situ techniques (use of gas and/or liquid, bias and heating STEM sample holders). Previous experience
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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image