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Field
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electronic conductivity can be made into dense ceramic membranes. Such dense MIEC ceramic membranes allow oxygen ions but not nitrogen ions to pass through. They are capable of separating oxygen from air with
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qualification degree in membrane science, chemical engineering, materials science or related field At least 3 years of relevant experiences in membrane separation and catalysis. Familiarity with two-dimensional
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1. PhD in Chemical Engineering, Materials Science, Mechanical Engineering, or a related discipline. 2. Knowledge of catalytic membrane reactors, membrane separation, or heterogeneous catalysis
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CBS - Development and Optimization of Separation Processes for Water Treatment and Resource Recovery
, and the reuse of treated water. This postdoctoral project focuses on developing, optimizing, and combining various separation technologies, particularly membrane processes, liquid–liquid extraction, and
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role in the Department of Physics at King's. The team brings together the Wallace Lab (membrane biophysics and single-molecule imaging, KCL Chemistry), the Photonics and Nanotechnology Research Group
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membrane technologies for bio-separation and biomanufacturing. You will focus on the development of porous affinity chromatography membranes, computational design and optimisation of affinity ligands
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Description The research position is part of the team working on advanced porous (MOF-based) materials and membranes for liquid-phase hydrocarbon separations, which is the key part of the new membranes and
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streams. NTNU leads WP1, Electrified Mono/Di-valent Stream Separation, which develops a membrane-based electrodialysis process for separating monovalent ions from divalent ions. The Postdoctoral Fellow will
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food technology with a focus on the development and evaluation of processes for the circular utilisation of by-products from dairy production. The research includes membrane-based separation and
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Osmosis (NF/RO) Membrane for Industrial Separation and Purification Application” Key Responsibilities: Integrating Machine Learning (ML) with Molecular Dynamics (MD) to predict phase-separation dynamics and