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& Wallenberg National Program for Data-Driven Life Science (DDLS), a 12-year initiative aimed at recruiting and training the next generation of data-driven life scientists and to create globally leading
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activities, which also include responsibility for the master's programme in Naval Architecture and Ocean Engineering and contributions to the education of seafarers. Your profile To qualify for this position
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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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build the sustainable companies and societies of the future. The Robotics and Artificial Intelligence (RAI) (www.ltu.se/robotics ) subject at the department of Computer Science and Electrical and Space
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the duties. The overarching goal of this research program is to uncover fundamental molecular principles governing membrane protein function, particularly how interactions with subunits, auxiliary proteins
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with our colleagues at the Department of Chemistry – Ångström, we are responsible for the Bachelor and Master programs in Chemistry, and the Master of Science in Chemical Engineering, together
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generation and attosecond physics Other skills, e.g. instrument programming, data analysis, vacuum technology, mechanical design Consideration will also be given to how the applicant’s experience and skills
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courses and educational programs in forestry and wood technology at both undergraduate and advanced levels. Subject area for the position: Postdoc fellowship in forest products Location until further notice
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the research fields above (e.g. human/economic geography, economics, political science, sociology, planning, urban studies or similar). The positions as Research Fellow require either a doctoral degree
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bioinformatics, physics, statistics, computer science, computational biology, or related fields. Experience programming in Python (or R) as well as bash/shell scripting. Experience with machine-learning and deep