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Field
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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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organizations, or government agencies on energy or infrastructure issues. Background in digital twin platforms, cyber-physical systems, or energy system modeling and optimization. Familiarity with large-scale
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systems. Key responsibilities Develop modeling tools to assess the environmental and socio-economic sustainability of industrial supply chains and processing technologies. Implement optimization models and
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on energy or infrastructure issues. Background in digital twin platforms, cyber-physical systems, or energy system modeling and optimization. Familiarity with large-scale energy or socio-technical datasets
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lysates Screening and optimization of complex media components for mammalian cell lines Development of serum alternatives Lead and coordinate research activities across multiple work streams Collaborate
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22 Apr 2026 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Engineering » Systems engineering Technology » Energy technology Researcher Profile Recognised
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and optimize sulfur cathode materials and related host structures Perform physical, chemical, and structural characterization of battery material Fabricate and evaluate lithium–sulfur cells in coin-cell
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systems, capable of designing and deploying innovative IoT solutions for research projects such as air quality monitoring, water leak detection and energy monitoring of electric vehicle batteries
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optimization. Your profile We are looking for candidates with strong academic backgrounds and a passion for sustainable, green energy processes and plasma process development. The positions require the ability
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 10 days ago
, energy optimization, and sustainable urban mobility. The position focuses on the development of a hybrid electromobility model within the eMob-Twin platform, combining large-scale mobility data from