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integrated circuits (EICs), and advanced packaging technologies. The co-design and optimization of these building blocks are essential for achieving breakthroughs in data rate, power efficiency, scalability
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renewable energy use, energy security, and the reliable operation of hydro-dominated power systems. The project will focus on how AI can support advanced optimization models for hydropower and energy-system
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Are you fascinated by application-oriented research in mathematics and eager to work at the interface of numerical optimization, optical design, and uncertainty quantification? In this PhD project
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modeling and optimization methods for the management of multi-energy platforms integrating electricity and heat systems within. The research aims at characterizing and valorizing the flexibility potential
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-time operation. The project will focus on how AI and mathematical optimization can be combined to support sequential bidding decisions under uncertainty. The initial use case will consider a wind power
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optimization of these building blocks are essential for achieving breakthroughs in data rate, power efficiency, scalability, and overall system cost. This PhD project focuses on the electronic integrated circuit
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capacity available to power markets with flow-based market coupling. Developing methods for optimally allocating the additional transfer capability enabled by increased transformer loading limits across day
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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration
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structure-preserving algorithms to generative modeling in AI. It will build upon the work done at IMF and SINTEF in this field. We will consider techniques like flow matching, and use ideas from optimal
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to the hazardous noisy non-convex optimization. • The scope of (new) applications is very large. Initially we will focus on qubit systems but in a second step we will consider Hubbard like models for strongly