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Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
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PhD in Materials Science Norrkoping Reference number LiU-2026-03852 Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic
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professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ Your work assignments Machine learning, and in particular deep learning, requires
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PhD student focusing on Machine Learning of Geometric Representations at ISY Linkoping Reference number LiU-2026-03853 We have the power of over 50,000 students and co-workers. Students who provide
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and apply AI and machine-learning methods to infer interaction networks from acoustic co-occurrence and activity patterns, and analyse their structural properties to link topology to response diversity
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involve developing and applying high-fidelity CFD models of turbulent flows in hydraulic turbines, integrating machine learning and deep reinforcement learning algorithms with numerical simulations
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mathematics, or a related field Practical experience with AI, machine learning, data analytics, or related computational methods Ability to apply technical knowledge to real-world problems Experience
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record of publications at leading venues in social science, machine learning, natural language processing, statistics, or computational social science is highly meritorious. You are a self-driven and
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involve writing and running code to process sequencing data on a computer server, applying statistical models and machine learning algorithms, and performing downstream data analysis, including
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inference of large AI models on GPU clusters. Experience with modern AI frameworks for large-scale machine learning, such as PyTorch, JAX, NVIDIA NeMo, Megatron-LM or DeepSpeed. Experience with GPU