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(on this project, funded by the Villum foundation, there will be hired 1 PhD student and 2 postdocs over a 5-year period). The postdoc will have opportunities to influence the direction of the project. About the
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currently composed of the PI, two PhD students and one technician. The group focuses on the study of bacterial stress response and bacteria-phage interaction. For more information, please visit the homepage
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these. We conduct research within nanophotonics, lasers, quantum photonics, optical sensors, LEDs, photovoltaics, ultra-high speed optical transmission systems, bio-photonics, acoustics, power electronics
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imaginaries. Research expertise in power dynamics and politics is also beneficial, as well as in sustainability research. The work involves close collaboration with the PI. The Department of Organization (IOA
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The Section for Software Engineering and Computing Systems, at the Department of Electrical and Computer Engineering (ECE), invites applicants for a two-year postdoctoral position within the area of
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, modelling, optimization and validation, and focus on: Develop thermally integrated storage and conversion systems, including Carnot batteries and/or high-temperature heat pumps based on power cycles. Design
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at conferences and meetings. Maintain high scientific rigor while fostering innovation and translational impact. Required Qualifications PhD in yeast synthetic biology, biotechnology, molecular biology, immunology
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the following essential experience and skills: Essential experience and skills: You have a veterinary background and a PhD degree preferably within biomedicine, immunology, neuroscience or related areas You are
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. Aarhus – a vibrant university city by the sea, combining rich cultural life with easy access to nature. Qualifications PhD in mathematics (completed or expected before start date). Preferred research
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/or high-temperature heat pumps based on power cycles. Design thermal and/or thermochemical energy storage systems. Implementing and validating advanced thermodynamic models for performance prediction