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- Universitat de Barcelona
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- Autonomous University of Madrid (Universidad Autónoma de Madrid)
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- Institut Català de Nanociència i Nanotecnologia
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optimize gene expression systems. Perform phenotypic and molecular characterization of engineered strains. Prepare scientific manuscripts, patents, and project proposals. Collaborate closely with
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codes is essential. Experience with modelling conjugated organic molecular-based systems (e.g. electronic structure, charge transport) would be particularly highly valued. Experience in coding with Python
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. The successful candidate will be joining the Molecular Nanophotonics group led by Prof. Dr. Niek van Hulst. Key responsibilities Contribute to Project FastTrack, to track energy (exciton/charge) transport in
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into polymer matrices. - Use of luminescent species for applications such as sensors. - Knowledge of DFT‑type simulation methods for modeling molecular properties. - Experience writing scientific articles and
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research communities. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques
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the EU Research Framework Programme? Other EU programme Reference Number PID2023-147939NB-I00 Is the Job related to staff position within a Research Infrastructure? No Offer Description Molecular modelling
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sciences. b) Strong background in wet-lab synthetic or molecular biology; experience in designing gene circuits or in modelling them is recommended, but not essential. c) Experience in writing articles and
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Responsibilities include: - Conduct molecular and cellular experiments using TAFs and related preclinical lung cancer models. - Identify new potential therapeutic targets and/or biomarkers associated with TAFs
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build using molecular dynamics, the MACE foundation models and density functional theory. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate
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. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques, experimental data bases and