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SD-26109- POSTDOCTORAL RESEARCHER IN AI-BASED ENERGY MANAGEMENT OF RESILIENT MICROGRIDS WITH SECO...
our website: https://www.list.lu/ How will you contribute? You will contribute to the ENERGY-RESILIENT-UA project, which aims to support vulnerable communities in Ukraine through resilient, low-carbon
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capable of automatically selecting the simulation strategy best suited to a given prediction objective, while optimizing the trade-off between accuracy and computational cost. The work will build on multi
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electrochemical interfaces for electrocatalysis and batteries. You will investigate how the properties of the electrode-electrolyte interface can be leveraged to optimize the activity, selectivity, and stability
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for autonomous optimization and design of materials, structures and processes Multimodal scientific AI – integrating language, images, sensor data and simulations Alignment for scientific AI – scalable oversight
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optimized physical and mechanical properties, in particular for energy-related (e.g., solar cell), aerospace and nuclear applications, where materials are exposed to ion irradiation. Although the effects
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focus on the molecular design, formulation, fabrication, and optical optimization of one-way transparent films. You will work with reactive liquid crystal mesogens, chiral dopants, photo-alignment methods
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understanding and enable the optimization of catalysts and experimental conditions. More specifically, the student selected for this position will be tasked with mapping the MHAT reactions of alkenes by
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Systems Engineering (ICE-1), our focus is on developing models and algorithms for simulating and optimizing decentralized, integrated energy systems. These systems are characterized by the high spatial and
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optimization and resource allocation schemes and algorithms for link and network optimization with hybrid fibre-FSO-RF communications. You will further augment software defined networking controllers
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full translational pipeline — from developing and optimizing cell enrichment and molecular profiling workflows, through single-cell characterization of circulating tumor-associated cell populations