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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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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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models, and reinforcement learning (RL), which is data-driven, are two powerful control techniques. MPC techniques are well-established, while RL techniques are gaining popularity due to increasingly
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circular plastics economy. Learn more about the project at https://inano.au.dk/about/research-centers-and-projects/enzync . If you are applying from abroad, you may find useful information on working in
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decision-making. Collaborate with international partners and contribute to joint research activities. Teach and co-supervise students at different levels in courses and associated projects. Publish and
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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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in learning it is a requirement. Experience in chemical carbohydrate synthesis and/or recombinant protein expression are considered advantageous. Excellent communication skills in English (written and
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results. The ability to work collaboratively in a team with an open-minded spirit, embracing both teaching and learning opportunities. A genuine interest in discussing physics and engaging in thoughtful
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to acquire greater knowledge about basic scientific problems and to conduct research oriented towards use in societies and companies. Technology for people DTU develops technology for people. With our