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Field
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-rounded academic background ◾Demonstrated ability to develop precision mechatronics system and algorithms ◾Ability to develop kinematic and/or dynamic analysis of mechanical/robotic systems ◾Ability
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the development of new algorithms for processing, analysis and inversion of active and passive seismic data and the application of these algorithms to field data. Student type Future Students Faculties and centres
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education . Skills and personal qualities In addition to the aforementioned requirements for the position: A demonstrable computational competence, comfortable with using and developing algorithms, data
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supply and a path to a net-zero-emissions society. SecurEL aims to develop new knowledge addressing research challenges arising from accelerated electrification and increased use of the existing grid
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processing, algorithm design, optimisation and simulation, software engineering and automation and control systems. An overview of the current PhD research projects is given here: https://www.dashh.org
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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the autumn of 2025 or as agreed. Project description This doctoral position has a special focus on sustainable data cultures, algorithmic decision-making and AI solutions within municipal companies
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. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop machine learning surrogates of wind energy systems. As newer
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programming Creating their own mechanical designs, implement and test them accordingly, Implementation of control algorithms on physical experiments. In addition, the candidates are expected to contribute with
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of the IMPRS reflects the development of molecular genetics into an information science, based on the plethora of experimental data that are nowadays available and steadily being produced about cellular