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letters of recommendation (submitted by the referees no later than the application deadline) Learn more about the BCBL’s OTM-R policy Additional comments Application process timetable: Deadline
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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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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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an open working model that combines remote working with work on-site, according to organizational needs and the nature of the tasks involved. About the UOC A leader in e-learning, our pioneering
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, spectropolarimetric inversion techniques, and machine-learning–based approaches, for the physical interpretation of solar images and spectral profiles. Special consideration will be given to applicants with experience
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for converged Wireless-opTical cOnfiguratioNs”. The research will cover three key pillars: Artificial-Intelligence (AI)-driven models in the access domain, focusing on CF technology to efficiently acquire Channel
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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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remote working with work on-site, according to organizational needs and the nature of the tasks involved. About the UOC A leader in e-learning, our pioneering university is a digital native with global
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(ICS). A scientific environment of excellence, highly dynamic, where high-end biomedical projects are continuously developed. Continuous learning and a wide range of responsibilities within a stimulating
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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