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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students
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to the lack of accurate models, machine learning-based problem solving is now revolutionizing almost every field of science and technology. FuturoChrom aims at developing model-free, purely reinforcement
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their PhD. Project description The aim of this project is to deepen the fundamental understanding of machine learning through the lens of optimal transport theory, systems theory, and statistical physics
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that learn nonlinear cross-fidelity correlations. More broadly, scientific machine learning methods such as physics-informed neural networks (PINNs) and operator learning (DeepONet, Fourier Neural Operator
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performance, yet their atomic-scale origin and role in reactivity remain poorly understood. The project addresses this open problem by integrating high-throughput Density Functional Theory, machine-learning
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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to work at Uppsala University. Duties The PhD student will carry out research in signal processing and machine learning with a strong emphasis on theoretical foundations. The PhD student will actively
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30th April 2026 Languages English Norsk Bokmål English English PhD Fellow in Machine Learning Apply for this job See advertisement About us The Nansen Center is a Norwegian environmental research
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expertise. 2. Curriculum Vitae including a list of publications (maximum 3 pages). Where to apply Website https://jobrxiv.org/job/phd-position-in-machine-learning-and-ecology/?utm_sourc… Requirements