23 digital-imaging-co-relation Postdoctoral positions at Chalmers University of Technology in Sweden
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protein engineering, synthetic chemistry, nucleic acid technology, and state-of-the-art biophysical methods (spectroscopy, live cell imaging and nanotechnology) to address cutting-edge questions in biology
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their own research agenda within some of the department’s core themes: Digital transformation and innovation strategy – how organizations adapt their innovation processes and strategic priorities in response
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programme Reference Number 304--1-14193 Is the Job related to staff position within a Research Infrastructure? No Offer Description This postdoctoral position focuses on securing large-scale distributed AI
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research group that views automation engineering as a key enabler for new methods and applications, driving and benefiting from the ongoing digitalization of society. Our research emphasizes social, economic
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-14184 Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a postdoc to join a project on carbon capture and utilization (CCU) at Chalmers. In
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-14170 Is the Job related to staff position within a Research Infrastructure? No Offer Description The Automation research group at the Department of Electrical Engineering is recruiting a postdoc to
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analysed using advanced optical techniques such as: High-speed imaging Laser-induced fluorescence Particle image velocimetry Experimental and numerical results will be evaluated and compared to refine both
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. Dedicated to advancing the field of Network Security, Secura Lab focuses on innovative solutions to protect modern digital infrastructure from emerging network threats. Please visit our website (securalab.org
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processes for CO₂ hydrogenation as part of CCU. The objective of the project is to develop novel catalysts for the process that will be examined in high pressure CO2 hydrogenation experiments, combined with
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digital twin framework, adaptable to: The level of detail available for ship modelling, The quality of risk-related data, and Quantified model and data uncertainties. The project will advance knowledge