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- Process data and develop predictive chemometric models - Prepare manuscripts, reports, and presentations for dissemination of findings within the scientific community. - Evaluate the obtained results and
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exploring the evolutionary trajectories of energetic frustration from ancestral to extant proteins, combining ancestral sequence reconstruction, structure prediction, and statistical energy models
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spectroscopy, especially applied to the analysis of lipids or oils. Experience in the application of chemometrics to develop predictive models Participation in competitive research projects related to the field
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approaches capable of guiding experiments, interpreting results in real time, generating predictive models of materials synthesis processes, and refining experimental strategies under a Human-In-The-Loop
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anticipation. Unlike static distributions, these models should represent the spatiotemporal transition functions of human states—predicting how a person will move or interact with objects(/persons) based
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. The successful candidate will be joining the Ultracold Quantum Gases group led by Prof. Dr.Leticia Tarruell . The Fermi-Hubbard model is a cornerstone model of condensed matter physics. It describes the physics
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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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bilingual exposure that accurately predict bilingual infants’ individual language outcomes. For this purpose, we will conduct large-scale longitudinal studies, combining a variety of neuroimaging, behavioral