28 multi-core Postdoctoral research jobs at Technical University of Denmark in Denmark
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to improve and optimize stem cell differentiation and manufacturing. As our new colleague you will be part of a diverse and multi-disciplinary team, which is engaged in machine learning and has a drive to make
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communication skills, and the ability to work effectively in multi-disciplinary teams are essential. Entrepreneurial interest or motivation toward start-up activities will be considered a plus. We value diversity
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cells, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences on electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery
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architectural design, including but not limited to ReACT/CodeAct agents, multi-agent systems, self-evolving agents, scalable agentic memory management and Extensive knowledge of existing bioinformatic algorithms
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, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences on electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials
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multi-physics modelling, autonomous materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD
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) networked multi-agent human-robotic systems that work collaboratively in a well-coordinated and safe manner, (b) computational design and digital manufacturing of components, (c) design of sustainable
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and maintenance of monitoring buoys and related sensor systems. Apply image analysis and machine learning techniques to ecological datasets. Develop and implement multi-platform monitoring frameworks
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neuroscience, objective measures of auditory function, computational models of hearing, hearing-instrument signal processing, and multi-sensory perception. Our goal is to advance the understanding of the human
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for sustainability, energy, and health. Our research efforts in advanced materials integrate novel synthesis and characterization techniques with state-of-the-art multi-scale modelling and scientific computing methods