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of heuristic models, mathematical programming, machine-learning and multi-objective optimization. Teaching includes basic and advanced courses within the subject, for example in the international international
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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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experience of application of artificial intelligence including machine learning and deep learning algorithms. Documented programming skills in Python, R, or MATLAB. Very good knowledge of English, spoken and
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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with other disciplines for developing deeper insight into the physical behavior of materials and structures, such as through combinations of finite element methods with machine learning. Tasks also
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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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approaches that combine artificial intelligence, machine learning, natural language processing, and social sciences. This collaborative and cross-sectoral approach aims to produce robust methods for evaluating
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learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a user fee-based support model. The projects will differ in complexity and length and will
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required. Proficiency in statistics and programming are highly meriting, especially in gene regulatory networks, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially
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to measure these backgrounds in data. The project also aims to explore to which extent machine learning methods can help with these tasks, e.g. object reconstruction and signal vs background discrimination