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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
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analyses and machine learning. Some data for the project already exist, but additional data will be collected from behavioural tests on privately owned pet dogs in Sweden and abroad (Europe). Travel and time
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work with large-scale behavioural data sets using a range of approaches, including heritability analyses and machine learning. Some data for the project already exist, but additional data will be
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facilitate data sharing among actors involved in a new circular flow of flat glass. Within the project, two PhD students, one at the Department of Computer and Information Science (with computer science
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computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes at bachelor’s and master’s levels, as
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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if you have worked with prediction models, machine learning or AI models and are familiar with blood cells such as neutrophils, leukocytes and platelets. Work experience in the area is meritorious. If you