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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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of the results The position is within the research group of Prof. Ingela Lanekoff that strives to develop and establish new innovative method within the research field of analytical chemistry. The research is
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funded by a EU programme Reference Number 304--1-14162 Is the Job related to staff position within a Research Infrastructure? No Offer Description Join a research team developing state-of-the-art open
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solving and technique development Scientific collaboration within and outside the group Communication and publication of the results The position is within the research group of Prof. Ingela Lanekoff
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation group. Main responsibilities Conduct research in collaboration with senior researchers and
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recently developed a new HHG source that delivers photon energies from 10.8 eV to beyond 25 eV at an energy resolution better than 20 meV. The combined high temporal, energy, and momentum resolutions will
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several
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modular, scalable, and transparent control algorithms suitable for real-time implementation across different vehicle platforms. - Contribute to theoretical developments in stochastic model predictive
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receive the benefits of support in career development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available