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Field
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. Weinstein (DTU Chemistry) to develop novel molecular ML methods and use them to control, steer, and learn from large scale wetlab experiments. You will deploy these methods in collaboration with Prof. Katrine
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components. Work closely with a beam diagnostics physicist and controls group engineer to install and optimize diagnostic systems. Develop and integrate mmWave diagnostics equipment for beam position
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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compositions (carbon, metal alloys, oxides, clays, polymers). The laboratory develops and characterizes these materials, seeking to understand and control their architecture (structure, organization
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develop technologies that contribute to human and planetary health. About the research project Bacterial protein-tyrosine kinases (BY-kinases) control bacterial biofilm formation, and they have no
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intelligence - Data-driven and learning-based control - Decentralized decision-making and distributed optimization - Belief-space and uncertainty-aware planning - Neuro-symbolic and context-aware reasoning
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photonic components and integrated laser systems. The postdoc will develop and optimize fabrication processes enabling low-loss waveguides, high-Q resonators, and efficient nonlinear devices. The role
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then be used in a learning framework to design an automatic controller to optimize the usage of the sluice under various conditions. The other project will address challenges related to Sustainable Water
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until filled. Required qualifications: The candidate must have a PhD in Quantum, Condensed Matter or Solid-State Physics (or related topics). References: [1] Universal control of a six-qubit quantum
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advanced process control strategies and deploy them on the center's distributed control systems and edge computing devices. Work closely with industry partners to validate and optimize the developed