43 molecular-modeling-or-molecular-dynamic-simulation Fellowship positions at University of Oslo
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competent candidate with strong experimental background in molecular biology and/or epigenetics that can work independently. The candidate should be able to collaborate in a research team of national and
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distributed data processing approaches to address issues of incomplete or redundant multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will
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PhD Research Fellow in Chemistry-focus on Experiments in Chemical Kinetics and Heterogeneous Catalys
conduct catalytic experiments using advanced transient methods based on mass-spectrometry and develop mathematical models to interpret these data in terms of molecular mechanisms. International
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dissecting the functionality of different EV subsets. Methods include, but are not limited to, in vitro culture of primary human cells and cell lines, organ-on-chip models, molecular biology, diverse
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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on developing new concepts in catchment–alluvial fan dynamics within a structural framework, with emphasis on sedimentology and petrography, and integration of complementary datasets, including seismic data
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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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the Department in subjects related to alluvial fan sedimentology, architecture, and petrography. The position will focus on developing new concepts in catchment–alluvial fan dynamics within a structural framework
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controlled common garden experiments, and analyze the molecular pathways involved in adaptation to extreme environments. While a core project framework is in place, there is flexibility to tailor specific
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(DPPs) as a key enabler of circular business models, aiming to reduce CO₂ emissions and improve resource efficiency through enhanced data-driven lifecycle management. A DPP can be viewed as a structured