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15 Sep 2025 Job Information Organisation/Company Seed Robotics Research Field Computer science » Cybernetics Computer science » Programming Computer science » Systems design Engineering » Control
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The Department of Electrical & Computer Engineering (ECE) at The University of Alabama is recruiting motivated PhD students to join our cutting-edge research program.Qualified applicants can
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23 Oct 2025 Job Information Organisation/Company Universidade de Coimbra Department SGRH - DRGC Research Field Other Researcher Profile Recognised Researcher (R2) Positions PhD Positions Country
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About the Opportunity This job seeks a research assistant to work on one or more projects related to the development and assessment of wearable robotics. Possible projects topics include: 1
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Center on AI for Decisions and Trust). Eligibility criteria PhD in Cybernetics, Control Engineering, Robotics, Computer Science, Statistics or related field. Demonstrated experience in research and/or
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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on performance, safety, and robustness of robotic and learning-enabled systems. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear
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Carl von Ossietzky Universität Oldenburg | Oldenburg Oldenburg, Niedersachsen | Germany | about 1 month ago
a powerful bridge between biology and engineering. The PhD offers training in computational neuroscience, neural circuit modeling, and AI/robotics, as well as strong interdisciplinary exposure through
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biofabrication system based able to self-assemble cellulose based composite materials under the control of genetic programming and a robotic fabrication system. A fermenter filled with cellulose producing bacteria
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems