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of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment
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, geometallurgy or related field Experience in either stochastics, deep learning or minerals processing is needed Structured and solution-oriented working style, analytical thinking and above-average committment
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month) will ensure participant compliance with study procedures. Under the direction of the PI and Program Coordinator, perform research-related administrative support tasks, including research data entry
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activated carbon filters using advanced analytical techniques. Project overview In this PhD project, you will analyse the removal of organic micropollutants (OMPs) from wastewater in biological activated
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to think and work independently, excellent analytical skills and strong verbal and written communication skills are also essential requirements. How to apply You must apply through the University’s Apply
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the mobility, ubiquity, security, and interactivity of computers, data, software, and users. The pervasive computing paradigm enables technologies such as sensors, actuators, and computers to take a back seat
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the mobility, ubiquity, security, and interactivity of computers, data, software, and users. The pervasive computing paradigm enables technologies such as sensors, actuators, and computers to take a back seat
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hold an MSc in electronics, computer engineering, or a closely related field. Required Qualification: Applicants must hold an MSc in electronics, computer engineering, or a closely related field. Strong
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want to contribute to the next level of molecular computing? Are you excited about the application of AI tools to train molecular systems how to process information? Then join our team as a PhD candidate
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning