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10 Apr 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation
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and management. Ability to visit clientele at sites throughout the county. Ability to plan and teach educational programs. Demonstrated leadership and ability to serve as a positive role model. Does
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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design
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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
conditions: Scientific area: Bachelor’s Degree in Systems Engineering. Work plan: Model-based digital twins supported by physics-informed machine learning for multiphysics modeling. 1. Grant duration and
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, physical models, and integration efforts Document configuration and operational procedures with precision and clarity Serve as a technical lead on projects, guiding architecture and design discussions across
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following areas: Strong foundation in machine learning, optimization, and deep learning algorithms, including Transformer architectures. Hands-on experience or solid theoretical knowledge of LLMs/SLMs
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic