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the redistribution of the phases present, reaction mechanisms will be investigated and the information gained will be used for optimization of the battery. The position is part of the INCEPTION Doctoral Network
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skills and a keen interest in data-driven research. Your role will be to apply the developed semantic infrastructure to concrete case studies—such as cross-unit scheduling, process optimization
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The primary tasks will be to: Synthesize thin films of solid-state battery components, following established protocols. Optimize the sensing schemes of a new state-of-the-art commercial scanning NV magnetometer
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pharmacology. The PhD project will focus on testing and optimizing antisense oligonucleotides in preclinical in vitro models, as well as formulating these molecules into a novel biodegradable delivery system
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structures, followed by thorough characterization of their chemical, structural, and mechanical properties. You will work on understanding and optimizing foam properties such as porosity, elasticity, and
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academic partners in Europe and globally. Candidates with the following qualifications will be preferred: Educational background in power systems. Documented knowledge in optimization in power and energy
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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
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Method to analyze the single-photon source performance (PhD1). Optimize and propose new single-photon source designs overcoming these limitations to be fabricated by other PhD students (PhD1). Perform
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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funded PhD scholarships (3-year employment) in a vibrant interdisciplinary research environment. The positions are part of the research project “AI-driven materials optimization for light trapping in thin