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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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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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. Pablo Carbonell, creates innovative solutions for automated biofoundries, fostering sustainable industrial biotechnology across the bioeconomy sectors. The lab combines synthetic biology, machine learning
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biofoundries, fostering sustainable industrial biotechnology across the bioeconomy sectors. The lab combines synthetic biology, machine learning, and biosensor and pathway dynamic regulation design to produce
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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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descriptors to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and
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computing and decentralized intelligence where a swarm of nodes learns graph dependencies by effectively integrating the structure of distributed systems into neural network architecture. This approach
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, multilevel analysis). Knowledge in developing predictive and forecasting models in health or environmental research. Skills in machine learning or AI techniques for prediction of complex outcomes. Experience
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university master's degree level or equivalent. Driving licence and car Not to have a previous PhD degree. Proficiency in English Strong teamwork skills, ability to adapt to multidisciplinary environments and