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. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled together will finally inform
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administrative, educational, and legal domains. The successful candidate will become part of the Centre for Machine Learning . The centre is part of the Data Science & Statistics section at the Department
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charges Thermochronometry and rock surface dating You will be part of the dynamic and interdisciplinary LUMIN team, which includes engineers, scientists, postdocs, and PhD students working collaboratively
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agreed upon with the relevant union. The position is a full-time position. The researcher position is part of DTU’s Tenure Track program. Read more about the program and the recruitment process here . You
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where variable renewable energy sources are involved. Emerging digital and cyber-physical systems technologies are an integral component of grid interactive efficient buildings. Through advanced control
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international conferences, is an integral part of the job. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree in
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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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defects and trapped charges Thermochronometry and rock surface dating You will be part of the dynamic and interdisciplinary LUMIN team, which includes engineers, scientists, postdocs, and PhD students
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Identification System (AIS) for ship detection. You must be fluent in English, both speaking and writing and possess excellent communication skills. As part of the Danish Ph.D. program, you will follow a number of
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impacts on target and non-target organisms and study the faith of the peptides in experimental settings. This PhD fellowship is part of the ENSAFE project (ENvironmental SAFEty of biotechnological plant