41 evolutionary-algorithm-"DIFFER" PhD scholarships at Technical University of Denmark in Denmark
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supervisors, from different universities: Dr. Georgios Tsaousoglou at DTU, and Dr. Maryam Kamgarpour at EPFL, Lausanne, Switzerland, with the opportunity to undertake an extended research stay at EPFL. Project
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more sustainable? If you share our passion for technology and the difference it can make in meeting the UN’s Sustainable Development Goals, perhaps you are one of our two new PhD students. At DTU Electro
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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the computational activities in a large closed-loop collaboration that includes computational, biotechnological and automation activities, requiring a solid understanding of the different areas involved in
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Job Description Do you care about fish welfare? Do you want to make a difference for fish and put their wellbeing on focus for the aquaculture of the future? DTU Aqua is seeking a motivated and
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will take advanced courses to build and deepen your skills, implement and evaluate algorithms, and develop your ability to write and present scientific work. We are a supportive team that will welcome
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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for Science & Technology (KAIST), and an external stay at KAIST will be included as part of the PhD program. Qualifications Proficiency with Python Experience implementing various Machine Learning algorithms
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the Section for Aquaculture, located at our campus in Hirtshals in the beautiful Northern part of Denmark. The section is a vibrant international group that holds a strong expertise in the different aspects
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experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly