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to work independently, with self-initiative, as well as collaboratively Ability and eagerness to learn new methods and a strong interest in developing both computational and analytical skills Desirable
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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) scientific studies Experience with relevant field data collection methods (e.g. chamber- or eddy covariance-based C flux measurements, biodiversity sampling methods) Computer programming skills (e.g. Matlab, R
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work. You should have a strong interest in molecular epidemiology and aging research, and be curious and motivated to learn new methods, skills, and concepts. You are self-driven and able to work
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. The student is expected to learn to design research questions and hypotheses, design experiments, analyze data, take courses, write scientific manuscripts, communicate science to their peers and the general
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the project’s research area Training in higher education teaching and learning Experience teaching Swedish and European economic history Documented administrative ability Assessment criteria The School
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research Strong knowledge of tumor biology Ability to interpret scientific literature, acquire new knowledge, and compile data Good collaboration and communication skills Excellent proficiency in English
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Initiatives in Forest Research (WIFORCE) program. The successful applicant will work on the development of bioacoustic monitoring methods using automated recording units (ARUs), deep learning methods, and
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machine data analytics to new harvesting system designs. The Department of forest genetics and plant physiology is part of Umeå Plant Science Centre (UPSC, https://www.upsc.se ), a world leading centre for
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care about creating a positive, respectful, and stimulating environment. We value communication and collaboration and a workplace that promotes learning and development for all employees. We are also