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Gionis. The position is part of the European project ARMADA funded by the Marie-Skłodowska-Curie Doctoral Network. The ARMADA project operates within the fast-growing field of Conversational AI, aiming
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aims to build predictive and physical binding models of protein – DNA interactions using high-throughput and quantitative biochemical binding data across hundreds of thousands of sequence variants
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for networking, and access to robust administrative and technical services—all within a setting that offers attractive employment conditions. To learn more about the department, please visit: https://www.umu.se/en
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materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose
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on behavioural syndromes and social networks in dogs and to some extent wolves. The selected PhD student will work with large-scale behavioural data sets using a range of approaches, including heritability
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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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-modal learning. From the materials science perspective, our primary focus will be on ultra-thin, so-called, 2-dimensional materials. This class of materials has unique properties which make them promising
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diverse community of individuals from a wide range of nationalities. As a PhD student with us, you benefit from comprehensive career development support, opportunities for networking, and access to robust
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behavioural phenotypes and social systems develop and evolve. Specifically, the project will focus on behavioural syndromes and social networks in dogs and to some extent wolves. The selected PhD student will
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) Computer Science/ Mathematics/Physics and at the second cycle level, 60 credits in Life Science, Computer Science Mathematics, Physics or Bioinformatics including a 30 credit Degree Project (thesis). Additional