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                data is therefore a merit. You are expected partly to implement and support already planned projects and partly to independently plan and implement projects, develop methods and support PhD students 
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                Engineering. The position comes with a substantial recruitment package from the Wallenberg AI, Autonomous Systems and Software Program (WASP). Welcome with your application, no later than 27 October, 2025 
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                of data-driven life science and includes access to a formidable package of resources. Project description: DDLS Fellows Program Data-driven life science (DDLS) uses data, computational methods and 
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                with researchers from different research fields and access to various scientific and technical expertise. All PhD students at the Faculty of Medicine attend the doctoral education program. More 
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                devote his-/or herself to his/her own education. More information about the doctoral education program at the Faculty of Medicine can be found at www.umu.se/en/faculty-of-medicine/education/doctoral 
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                . The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-year initiative that focuses on data-driven research, to train and recruit the next generation of life scientists and 
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                the Wallenberg AI, Autonomous Systems and Software Program (WASP). Application deadline is 10 August 2025. Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research 
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                to complete the work duties satisfactorily. The criteria for the evaluation of administrative expertise are: Experience as a program or course coordinator Demonstrated ability to create collaborations between 
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                (contact details, employment history and education) an account of research experience (including a list of articles published and a brief research plan) an account of pedagogical experience and activities 
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                multidisciplinary theme. The program prepares next-generation computational biologists with strong complexity thinking and tools applicable to living systems across organizational scales, addressing major