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life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health
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program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes
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to treat colleagues and students with respect, take responsibility for their work and contribute to a good working environment. We value the qualities that a balanced distribution of age, gender, and
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, dynamic programming , statistical signal processing, reinforcement learning, and have good programming skills in Python and MATLAB. - Ability to work independently and ability to formulate and tackle
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)—that is, the inability of a model to effectively process or understand visual information. This work involves integrating visual encoders with language models to create multimodal systems. The emphasis is
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methods based on optimal transport for addressing problems in signal processing, control theory, and inverse problems. The doctoral student project and the duties of the doctoral student By developing novel
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, computer engineering, human-computer interaction, or equivalent by 2025-07. Demonstrate proficiency in English (reading, writing, speaking). Show the ability to work independently as well as in a team. Good
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. Application procedure Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than August 10th 2025. Applications and documents received after
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) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures
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. The data-driven life science initiative Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular