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Field
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made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria Solid understanding of control theory, including classical and
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statistical methods for adaptive learning, sequential decision-making, and control, motivated by applications in public health and epidemiology. The project will focus on methodological advances in
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with a strong background in applied mathematics, control theory and/or optimization to apply for a fully funded 4-year PhD position in the Smart Manufacturing Systems (SMS) group at the Engineering and
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the work aligns with your goals and aspirations. For more details see https://binyshlab.com/positions/ What will you do? Conventional robotic bodies rely on computationally intensive centralized control and
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Quantum computers have grown remarkably fast. Qubit counts have risen year after year, driven by advances in fabrication, control electronics and system integration. Quality, however, has not kept pace
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environment and humans during the execution of specific tasks. Project description The vision of RAI is aiming in closing the gap from theory to real life, while the team has a strong expertise in field
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environment and humans during the execution of specific tasks. Project description The vision of RAI is aiming in closing the gap from theory to real life, while the team has a strong expertise in field
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for the position. Preferred selection criteria Solid understanding of control theory, including classical and modern control design methods Experience with modelling and simulation of dynamic systems Programming
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systems, but the field is broad and is related to many other areas of mathematics, including measure theory, number theory and algebraic and complex geometry. As a PhD student in this project, you will
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on the intersection of learning theory, PDEs, and systems & control, likely using RKHSs (or similar function spaces), Koopman operators, and neural networks to study interesting classes of controlled PDEs, develop