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of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree. Qualifications needed To be eligible for the PhD programme, you must have completed a
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position is for a fixed-term period of 3 years with the possibility of a 4th year with compulsory other work (e.g. teaching duties at the Department). About the project/work tasks: The theoretical physics
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on mutation influx and in how we can identify cancer driver events from observed mutation data. To this end, we develop mathematical and computational approaches to estimate mutation rates, tumor growth
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project will focus on one or more of the following directions: Design methods to enable individual clients to evaluate semantic queries to retrieve data and train models in a federated environment while
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physics at the Department of Physics and Technology . The position is for a fixed-term period of 3 years with the possibility of a 4th year with compulsory other work (e.g. teaching duties at the Department
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advertisement This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim
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university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work
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– one that is truly of Technology, of Innovation and of Entrepreneurship, and proud of it. We are committed to a differentiated university proposition in education and research, so that: Every Swinburne
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differentiated university proposition in education and research, so that: Every Swinburne learner gets a work experience Every Swinburne graduate gets a job Every Swinburne partner gets a tech solution Swinburne
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investigating the learning behaviour of neural networks from both a theoretical and experimental angle, you will help us to better understand their limits and potentially develop improved learning algorithms. In