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, or reinforcement learning Proficiency in Python and contemporary software ecosystem for optimization, dada management, and API development Experience with teaching and supervision Experience with IoT, edge computing
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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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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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: Utilize Python, R, SQL, and containerization (Docker, Kubernetes) to run data ingestion and processing within automated data pipelines Optimize data processing workflows, reducing manual intervention
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partners and optimize them for nonlinear quantum processes Collaborate with theory colleagues to refine fiber designs based on experimental feedback Disseminate results at international conferences and in
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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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coating production and coating use. Test methods Reliable and fast test methods to optimize coating performance is of utmost importance in coatings development. We work on new test methods and optimisation
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the optimization of bioactive compound extraction from various seaweeds for the development of fortified foods. This PhD position aims to (i ) identify suitable seaweed species for high-yield protein extraction, (ii
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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