124 computer-"https:"-"https:"-"https:"-"https:"-"Dr"-"UCL" Postdoctoral positions in Denmark
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computational scientists. The position offers a unique opportunity to work at the interface of landscape ecology, biodiversity science, climate mitigation, and sustainable agriculture, contributing directly to
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, proteins and DNA origami constructs, and computational procedures for data analysis. The project is a collaboration between the single molecule biophysics and chemistry group at iNANO/Department
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laboratories, greenhouses, semi-field, and field-scale research facilities, advanced computing capacities as well as an extensive national and international researcher network. The department consists of nine
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Environment. CEBE is a strategic initiative funded by the Villum Foundation that seeks to strengthen research and development of sustainable solutions for the construction sector. The programme will run for 10
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friendly atmosphere of large and interacting communities for e.g. structural biology, molecular cell biology, computational biology, neurobiology, and molecular medicine that encourages lively, open, and
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computational scientists. The position offers a unique opportunity to work at the interface of landscape ecology, biodiversity science, climate mitigation, and sustainable agriculture, contributing directly to
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University also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here . The application must be submitted via Aarhus University’s
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, proteins and DNA origami constructs, and computational procedures for data analysis. The project is a collaboration between the single molecule biophysics and chemistry group at iNANO/Department
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biosynthetic pathways for microbially-derived molecules and understanding how microbial molecule production can be modulated for disease prevention and therapeutic benefit. Using a combination of computational
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will