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of the mechanisms behind model improvements, and paleoclimate simulations and climate projections. Should be familiar with CESM, WRF, SPEAR, CM1, and theoretical atmospheric models, as well as the land modules within
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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-chain operations. Key Responsibilities: Design and implement Hyperledger Fabric and Avalon-based smart contracts with agentic AI integration. Develop dual-hash validation mechanisms to ensure ledger and
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FCT. solid experience in computational modeling, with relevant skills for the development of models and projects involving photovoltaic systems. Workplan and the objectives to achieve: The main
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five years of post-qualification experience at the time of application; (b) academic background and solid knowledge of Mechanical Engineering, Aerospace Engineering, or a related discipline, with a
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Engineering or Energy Engineering or relevant field. A solid background in dynamics and mechanical system modelling is essential. Applicants must demonstrate their research capabilities and ability
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Science, Mechatronics, Robotics, Mechanical Engineering, or a related field. At least 1 year of relevant technical hands-on experience in a similar role or specific technical scope. Solid knowledge and
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method thus enables, for a given granular packing, independent control of the mechanical behaviour of the binder and of the amount of binder deposited, both as solid bridges at intergranular contacts and
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
on identifying biomechanical tipping points at which invasion becomes mechanically unfeasible. The work of the PhD fellow will be theoretical and computational in nature and include: Developing physics-based
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova