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motivated co-workers with expertise in cutting-edge technology. Your mission We are looking for an outstanding multi-omics bioinformatician with strong analytical and programming skills working on cancer
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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 create strong and globally
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of Cultural Sciences and is affiliated with the national research programme Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS). It is also part of the research cluster AI
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, second and third cycle levels. The department is responsible for ca. 30 courses within SLU's educational programs, e.g. the agronomy programs, the bachelor's program in biology and environmental science
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process and disseminate information about opportunities: from identification of suitable major calls and grant opportunities (especially international, and especially within the EU Framework Programme, now
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friendly, curious and skillful staff with different backgrounds. Your main work tasks will be Develop the serial crystallography user program at MicroMAX together with the MicroMAX team and the MX group Lead
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processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data Driven Life Science (DDLS) aims to recruit and train the next generation of data driven life scientists
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innovation come together. Our unique lab environments and expertise represent major parts of the textile value chain. Close cooperation with the industry shapes our educational programs, making the School
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-based analysis of the samples with cell-based assays. Qualifications To qualify for this appointment as a teaching assistant, you must be admitted to a study programme (master's level). It is also an
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areas, such as scientific and educational development, leadership development as well as patent and innovation support. The position is part of the SciLifeLab Fellows program and will be placed within