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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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challenges of learning from network traffic, (ii) train original AI models that are designed to operate precisely on such data, and (iii) demonstrate the viability in production of AI-driven solutions for, e.g
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PhD Position – Development of metal-organic frameworks-glass composites for sustainable applications
to be interested in multidisciplinary work and motivated to learn from a variety of scientific fields. Experience in the field of metal-organic frameworks (MOFs) and crystallography is desired. Experience
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Institute of Health (ICS). A dynamic scientific environment of excellence, where cutting-edge biomedical projects are continuously developed. Continuous learning and career development pathways. Flexible
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, environmental, and resource economics. Ideal candidates are those wishing to acquire research experience for a subsequent Ph.D. in Economics. Due to the short term appointment, applicants must have a Spanish tax
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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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expertise in analysing complex quantitative and computational data and/or rich qualitative data. • Proficiency or skills to learn a statistical software (e.g., R, Stata, Python). • Interest in contributing
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. Gramaglia, P. Serrano, C. Mannweiler, “ATELIER: service tailored and limited-trust network analytics using cooperative learning”, IEEE Open Journal of the Communications Society S. Henri, G. García-Avilés, P
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workflows). Ability to work effectively in English within an international research environment. High learning agility and a structured approach to complex problem-solving. Assets (Desirable, but not
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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