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Field
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute to advancing efficient AI systems. Are you motivated to take a step
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20 Aug 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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of machine-learning-infused atomistic modeling techniques and their application to important problems in chemistry, physics and materials science. Together, you will help advance a key scientific
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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experience in Python and, more specifically, in common deep learning frameworks such as PyTorch and jax, for model training and inference have experience with embedded platforms such as FPGAs or RISC-V
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is seeking a PhD student to join an ongoing Royal Society–Research Ireland University Research Fellowship project focused on Machine Learning for the Design of Additively Manufactured Two-Phase Heat
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, especially in quantitative subjects • Strong Python skills and experience with deep learning frameworks, preferably PyTorch • Solid foundations in machine learning, statistics, linear algebra, and model