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tools to predict the functional specificity of Carbohydrate Active Enzymes (CAZymes) and proteases. The overall goal is to enhance our understanding of seed carbohydrate content and composition, and to
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unpredictable operational conditions. The successful candidate will have a solid foundation in AI methods for predictive and/or proscriptive problems and will demonstrate the ability to use this background
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Job Description Are you excited about developing advanced models and algorithms to better understand and predict human decision-making? Are you interested in integrating behavioural models with
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building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy models, and reinforcement
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technologies of the department, e.g., batteries and catalysts. The project includes collaboration with experimentalists at DTU, who will verify the computational predictions as well as Saltfoss Energy, our
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background checks may be conducted on qualified candidates for the position. The Research Group for Genomic Epidemiology conducts targeted research with the aim of predicting and preventing infectious diseases
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prediction models governing TCR interactions. However, overall the accuracy is limited, and characterizing and predicting these interactions remains a significant challenge. This, we postulate, is due to three
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for the position. The Research Group for Genomic Epidemiology conducts targeted research with the aim of predicting and preventing infectious diseases in humans and animals, as well as supporting global detection
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dedicated to developing prediction models governing TCR interactions. However, overall the accuracy is limited, and characterizing and predicting these interactions remains a significant challenge. This, we
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17 PhD positions - Marie Sklodowska-Curie Doctoral Candidates within the ‘FRESCO4NoPain, Frontier Re
DC10: ‘Using tRNS to modulate cortical excitability and the experience of pain’, PainLabMunich, The Technical University of Munich, Germany DC11: ‘Assessing clinical and electrophysiological predictive