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Scanning Calorimetry (DSC), High-Performance Liquid Chromatography (HPLC), and related techniques. Demonstrated ability to prepare, process, and analyze polymer samples. Familiarity with experimental design
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of the Water Research Center. The successful applicant will lead an innovative project focused on the development of hybrid processes for the recovery of minerals from desalination reject brine. The role will
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background and expertise in one or more of the following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural
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processes. Developing and optimizing functional membranes, including electrically conductive membranes, for use in desalination, energy generation, and electrochemical separations. Responsibilities: Conduct
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. Contribute to the smooth operation of the laboratory. Participate in the academic activities of the division. This position does not include teaching duties and the SSEL provides financial support for travel
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. The successful applicant will contribute to four main research thrusts that are central to the success of this multidisciplinary project: (i) designing and developing chemical and ion-exchange processes for energy
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through computer simulations and/or experimental validation. The PDA is expected to actively disseminate results through publications in high-impact journals and presentations at leading international
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competitive salary and benefits. Research in the SIT-D lab focuses on understanding and modelling consumers' behavior and decision process for sustainable transport modes and transport innovations. Research is
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candidate’s societal impact and outreach activities. Contact information of 3 references for recommendation letters to be solicited at a later stage of the search process. Do not include the letters in
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at the intersection of artificial intelligence and cultural heritage. The successful candidate will be involved in cutting-edge research and development in 3D computer vision and machine learning for the digital