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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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into the enzyme-substrate interaction in the same set of enzymes. Further, there will be collaboration with a PhD student from NTNU working with similar analytical methods on another class of carbohydrate-active
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behaviour and provides active personalised learning for improved instant decision making. Key beneficiaries are expected to be construction industry stakeholders, for example, project owners, architects, engi
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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of the research group. Desired qualifications and skills: A relevant background in aquatic biology, animal physiology or a related field. Good skills for laboratory-based analytical tools. Practical experience
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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tasks will be to: Genetic engineering of bacteria. Phenotypic characterisation of engineered strains. Teach and supervise BSc and MSc student projects. You must have a two-year master's degree (120 ECTS
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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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understanding of the process, statistics and data analytics shall be applied to link the different conditions to the likelihood of microcracks occurring. The severity of microcracks may also be studied in