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and Northeastern University, USA. Responsibilities The PhD project involves developing a flexible vegetation model within the OpenFOAM platform, where vegetation stems are represented as nonlinear
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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Foundation Models initiative . The proposed starting date is 1 September 2025 or soon thereafter. The appointment will be made for a term of three years at a competitive salary and will follow the PhD study
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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the Computer Science study program. The stipend is open for appointment from August 1st 2025 or soon thereafter. The PhD students will be working on topics within the general areas of formal methods, model checking and
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with the aim of making it a practical computational tool in the near term. The project will focus on Gaussian Boson Sampling (GBS), which is a specialized photonic quantum computing model with
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-cutting and bending to break the glass panels. The project will involve the establishment of a numerical model and the acquisition and analysis of data from physical measurements in the production
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic