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model adequacy Experience with development of R packages Familiarity with RevBayes and probabilistic programming Ability to collaborate in interdisciplinary projects The application should consist
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. The research group is part of the National Program for Data-driven Life Science (DDLS), generously funded by the Knut and Alice Wallenberg Foundation: www.scilifelab.se/data-driven/ Our group focuses on studying
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to the application deadline. PhD in computer science, electrical engineering, biomedical engineering, or a related field. Experience in Python programming, natural language processing, and multimodal deep learning
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articles for publication in international peer-reviewed journals within the relevant field Actively contribute as a co-applicant for research funding and develop independent grant applications Your profile
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. Demonstrated skills in Python programming, or other computer programming. Strong interest in data science, such as data collection and curation, modelling. Excellent written and oral English communication skills
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into consideration. The applicant should have good knowledge of automatic control, optimal control, statistics, and optimization. Furthermore, good programming skills are required, including Python and C++, and
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RevBayes and probabilistic programming Ability to collaborate in interdisciplinary projects The application should consist of the following: A personal motivation letter detailing your merits in relation
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ecosystem dynamics. This is a full-time, two-year position. The application deadline is May 5, 2025, and the expected start date is September 1, 2025, or as agreed upon. Project description and job duties
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The Camunas-Soler lab is opening scholarships for two Postdoctoral Research Fellows to join our interdisciplinary research team (www.camunaslab.org ). These positions are part of a broader program
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national and European projects that focus on both fundamental and applied research. The Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) addresses data-driven methods to gain