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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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applied machine learning projects in, e.g., computer vision, in close collaboration with industry partners. The position is not connected to an existing project, so the postdoc fellow will either join an
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, Environmental Sciences, Computer Sciences Proven experience in financial economics, financial risk assessment as well and in climate and ESG ris Interest to work at the interface between research and policy, and
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Applicants (student or postdoctoral fellow, home and host supervisors) submit their application to the partner organization or to Mitacs 16 weeks prior to the project start date for processing. Within 12 weeks
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-on experience with small animal handling and surgery, confocal microscopy, and molecular/biochemical assays (e.g., RNA/DNA isolation, qRT-PCR) Excellent verbal and written communication skills The ability to work
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and running of laboratory and/or experimental techniques, equipment, and processes as appropriate. 5. Develop and enhance your research profile and reputation and that of the University of Glasgow
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, development, and execution of computer simulations of system related to different replication stages of the HIV virus. Preference will be given to applications with expertise in 1) protein dynamics; 2
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grasslands. What we expect from you • University degree (M.Sc.) and Ph.D. in Agricultural Science, Biology, Ecology, Environmental Science or a related field. • Thorough understanding of the processes
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-driven short stack test rig for the experimental investigation of the influence of pressure on r-SOC degradation processes. You are welcome to use the freedom of the project to set your own priorities
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Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR