133 programming-"the"-"DAAD"-"U"-"Prof"-"UCL"-"Brookhaven-Lab"-"CSIRO" positions at Technical University of Denmark
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will be enrolled in one 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
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-time position. The assistant professor position is part of DTU’s Tenure Track program. Read more about the program and the recruitment process here . You can read more about career paths at DTU here
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in glaciology, Geodesy, Earth sciences, remote sensing, or a related field. Strong skills in data analysis, programming, and handling large geospatial datasets are essential. Experience with satellite
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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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stability, and computational efficiency. It is expected that the coding will be done with an open-source programming language. The field experiments have the aim of validating the modelling; they will involve
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explored using scientific machine leaning. Machine learning, programming experience and a curious mind-set You are fascinated by how computers can learn from data and you have a strong interest in the
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advice and provides educational programs and service to society. We are working to develop new environmentally friendly and sustainable technologies, methods and solutions, and to disseminate
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to engage in interdisciplinary collaboration and teaching. Good programming capabilities in advanced analysis software (e.g., Python, R, MATLAB, Julia, or similar) and mathematical optimization suites
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technology for people. With elite international research and education programs, we aim to create a better world and tackle global challenges. Founded in 1829 by Hans Christian Ørsted, DTU continues to pursue
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: Develop your own PhD plan outlining experimental trials. Design and conduct in vivo trials with salmonids. Collect biological samples and observational data from lab-based studies and at real fish farming