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collated due diligently. These tasks require particularly good English and organizational skills data collection and analysis tasks. These tasks require competencies in mathematics, data analysis and basic
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interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and statistics. This PhD project falls under Research Thrust RT2 on Physics-based
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, applied mathematics and computational sciences with a strong focus on computational humanities research. The project leverages an extensive dataset of digitized books, newspapers, and visual artworks
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candidate should hold a Master's degree in either Data Science, Applied Mathematics, Business Administration and Economics or a related field with strong programming skills, particularly in Python. Priority
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individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics
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the M.Sc. in Business Administration and Mathematical Business program. The assistant professorship is a six-year tenure-track position with research and teaching obligations, and with the possibility
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individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics
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, geophysics, mathematical modelling, or related fields. The ideal candidate should have: Experience with numerical climate models (such as EC-Earth or similar GCMs). Advanced programming skills in Python
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, geophysics, mathematical modelling, or related fields. The ideal candidate should have: Experience with numerical climate models (such as EC-Earth or similar GCMs). Advanced programming skills in Python
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Modelling using CAD design and simulations, and mathematics. A strong motivation for collaborative projects within academia and industry Proficiency in Danish is required due to collaboration with local