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be enrolled in Aalborg University Business School’s Economics, Business, and Management PhD program. The position is part of the Carlsberg Foundation-funded Green Transition Policy Centre (GreenTraC
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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
is on understanding and improving the performance of classic learning algorithms, in particular Boosting and Bagging, both in terms of speed and generalization capabilities. The project also allows
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will organize several research-related activities, such as workshops and seminars throughout the project period. The work is supposed to empirically test hypotheses rooted in economic theory. The center
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, graph theory, satisfiability problems, discrete optimization. Strong interests in chemistry as well as proven competences in programming and ease with formal thinking are a necessity. This PhD project is
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for Health Economics (DaCHE), Department of Public Health, University of Southern Denmark. You will work closely with Senior Health Economist Line Planck Kongstad and Professor and director of DaCHE, Dorte
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, human, and sustainable approach that ensures integrity in our work. Therefore, we strive for and take responsibility for driving the democratisation of digital technologies, so that everyone has
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during training, an effect attributed to the properties of the optimization technique. Intuitively, stochastic optimizers tend to converge to flatter minima in the complex loss landscape, which is believed
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traditionally relied on discrete active and passive components, with manufacturing processes remaining largely unchanged and labor-intensive. As part of the ERC project H3PMAG, this project aims to develop
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.), especially for international candidates through our international staff office (ISO) and the administrative staff at POLIMA. For further information about the positions and the research center, contact
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integrating machine-learning techniques with experimental datasets on bioplastic degradability. You will work to establish links between polymer features and degradability through mapping of existing data