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Knowledge of emerging methods for analysing cosmic dust particles Expected Start Date 1 November 2026, or as agreed Duration of the Position 12 months Required Application Materials The application must
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aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain. The Swammerdam Institute for Life Sciences (SILS) is located at the vibrant Amsterdam
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Responsibilities The Singapore-MIT Alliance for Research and Technology (SMART) Centre invites applications for a Postdoctoral Associate position in the area of crystallization science and particle
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, manufacturing and scale-up decisions, with the production of white paper/s, The role is for 12 months and will focus on understanding and mitigating aggregation and particle formation in biologics, with research
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sphingolipid transport systems. You will determine high resolution single particle cryo-EM structures of sphingolipid transporters. In parallel, you will assess sphingolipid-transporter interactions and the role
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mixtures, including information retrieval, random access, approximate search, querying and filtering, encryption, compression, and many other information-processing tasks. Our team is looking for a talented
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and mitigating aggregation and particle formation in biologics, with research themes including: How formulation variables (excipients, surfactants, buffers, lipids) drive instability and particle risk
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for this position have a great interest and knowledge in data analysis, filtering and modeling based on large amounts of data. It is important that you have a solid understanding of mining and mining systems to also
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the world works, be it elementary particles, the birth of the universe or the functioning of the brain. Want to know more about our organisation? Read more about working at the University of Amsterdam
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-particle physics, predictive modeling, and AI-enabled digital twins for magnetically confined fusion plasmas in tokamaks. The research will combine high-fidelity simulations, reduced transport modeling