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has gone through a very rapid development in the last few years. Large-scale machine learning models are however notoriously over-confident. With insufficient amounts of data to train them on together
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both experimental development and theoretical modelling. Therefore, it requires the candidate to have a solid background in physics, electronics, and mathematics, along with strong practical experimental
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en
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: Analyze spectroscopic and kinetic data, employ statistical and machine learning approaches where relevant, and contribute to manuscripts, presentations, and reports. Collaboration: Work closely with project
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that you will help us to build the sustainable companies and societies of the future. The Machine Learning Group at Luleå University of Technology seeks a doctoral student in machine learning. We offer well
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collaboration with Lund University. The candidate is expected to have a strong mathematical background particularly in stochastic modeling, optimization, and reinforcement learning. As a PhD student, you devote
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be in advanced courses in computer science, mathematics, AI, machine learning or similar. Alternatively, you have gained essentially corresponding knowledge in another way. The applicant is expected
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. - Apply graph-based and machine-learning tools to model signaling networks driving plasticity. - Validate candidate pathways in cell culture and mouse models. - Present findings at seminars and conferences