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:10.1093/nar/gkaf1388), we will develop machine learning tools to model microbial communities and their impact. The environment: The successful applicant will work within the Hildebrand and Traka groups
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The aim is to develop machine-learning models that describe how
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: comparative omics, genetic diversity analysis, mathematical modelling, machine learning, and the use of model organisms. Develop transferable skills such as scientific communication, project management
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to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust, interpretable models from experimental and operational data
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contribute to the development of innovative, physiology/ machine learning-driven clinical solutions and decision support tools for critically ill patients, focusing on cardiovascular and respiratory monitoring
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to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning
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they are mainly based on predetermined rules of behavior chosen by the designer. More recently, methods derived from machine learning provided impressive results. However most are datadriven, meaning
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models
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voluntary tax-deferred savings options Employee and dependent educational benefits Life insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http
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). For more information: https://www.cordis.europa.eu/project/id/101225380 Research focus The PhD candidate will work on one or more of the following interconnected areas: AI‑ and machine‑learning‑based