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Field
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Environment Standard office environment. Unexpected interruptions occur often and stress level is moderate to high. Noise level is quiet to moderate. Physical Activities Ability to work in front of a computer
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synthetic biology approaches to address the current challenges in natural product discovery. Through genome and enzyme engineering, we design high-performance microbial strains to accelerate the discovery and
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instrumentation, data post-processing, analysis and management for experimental projects conducted in the Icing Wind Tunnel. The role will also include the dissemination of research through publication in high
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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motivated researcher seeking to build leadership, translational impact, and mentoring experience within a high-profile research program. About you: The successful incumbent will be a driven and accomplished
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) Application Deadline 1 Mar 2026 - 00:00 (UTC) Country Singapore Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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to the creation of knowledge in the field of early cognitive and social-emotional development, by undertaking a specified range of activities within an established research programme and/or specific research
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cross-national exchange and collaboration. The program is open to eligible individuals from the following countries: Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Singapore
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the Department of Cancer and Genomic Sciences. Their main role will be to apply and develop new methods of precise genome manipulation and subsequent in vivo phenotyping (e.g. high resolution 4D imaging) and
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decarbonization, grid modernization, and the integration of distributed and flexible energy resources. Research topics may include—but are not limited to—AI-based grid operation and planning, reinforcement learning