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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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ocean, and an urgent need to inform regulatory bodies about associated environmental risks. The work builds upon methods developed in our previous inter- and transdisciplinary work on shipwreck risk
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Technology, Campus Norrköping. Your work assignments This position is motivated by the need for reliable visualization and data analysis methods that support understanding of the increasing amount
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methods to integrate user behaviour into LCA and design practices. What you will do: Pursue doctoral studies leading to a PhD degree Conduct LCAs of household appliances integrating user behavior Contribute
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for culture and creative domains. These domains may include sound, music, visual art, 3D graphics, movement, or multimodal combinations thereof. The research will employ a mixed-methods approach: the search
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aluminium. The candidate will investigate various methods for symbolic regression, aiming to extract symbolic information, like mathematical functions or programs from a network trained for material modelling
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) analysing the effects of different trade-offs between timber production and biodiversity under the influence of climate change, and 3) developing optimisation models based on heuristic and AI-based methods
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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information about us, please visit: the Department of Biochemistry and Biophysics . About the DDLS PhD student program Data-driven life science (DDLS) uses data, computational methods and artificial