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in simulation of robotic systems Experience in design and development of robotic systems Experience with using computer vision. Furthermore, the successful candidate is expected to: Have excellent
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that connect local chemical environments and correlated disorder to thermodynamic stability and transport. The research will form a central part of CENSEMAT's simulation-guided materials programme and
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Postdoc in first-principles modelling and machine-learned force fields for correlated disorder in...
thermodynamic stability and transport. The research will form a central part of CENSEMAT's simulation-guided materials programme and will be carried out in close collaboration with experimental researchers
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following competencies and skills are required: Carrying out experiments using the hybrid testing method. Development of multibody dynamic simulation software using modern approaches (e.g., Kane’s method
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collaborate on ongoing modelling and simulation initiatives for wildlife across the department and beyond, such as work under the European Goose Management Platform To organise a technical workshop to
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nitrogen dynamics, and climate change mitigation potentials in agroecosystems . You will be contributing specifically to the area of regional simulation using process-based models and advanced
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multibody dynamic simulation software using modern approaches (e.g., Kane’s method). Implementation of control algorithms and robotics for controlling hexapod-like loading systems. Excellent English
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Computer Science Ref number: 2026/958 Employment type: Full Time Apply Aalborg Deadline : 31.08.2026 Department: Department of Computer Science Ref number: 2026/958 Employment type: Full Time Apply Vacant
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outreach program for stakeholders engaged in the conservation of endangered waterbirds To collaborate on ongoing modelling and simulation initiatives for wildlife across the department and beyond, such as
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and