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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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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
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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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and kinetic modelling Expression, purification, and characterization of enzymes from fungal and bacterial sources Development and optimization of enzyme assays Structure–function studies of enzymes