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Research Infrastructure? No Offer Description Topic background A proper functioning drinking water distribution network is of major importance for society and keeping this network optimally operational is
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(e.g., regeneration agents, acids, antiscalants), leading to high costs and environmental concerns. Our challenge is to develop a desalination scheme that produces high-quality water and multiple
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expertise in wireless communications, communication theory, information theory, applied probability, and optimization • Excellent written and verbal communication skills Preferred Qualifications • Prior
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are working closely with coastal communities to co-develop this platform and improve resiliency to climate events. Multiple robots will autonomously gather and transmit data real-time over a cellular network to
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optimization. Experience with energy system modeling - ideally of large scale multiple country energy systems, PtX and renewable fuel production. Strong writing and presentation skills. A willingness and desire
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optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and Mærsk McKinney Møller Center for Zero Carbon Shipping (Denmark
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aims to develop a novel theoretical framework for nonlinear and robust control of dynamical systems from a phase perspective. You will have the opportunity to freely explore multiple research directions
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling Job No.: 683222 Location: Clayton campus Employment Type: Full-time Duration: 3.5 to 4-year fixed
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therefore focus on a key challenge in this domain: the design of next generation decision support tools for battery operators, focusing on the participation in multiple consecutive short-term electricity
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal