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- Participatory Methods for Impact chain assessment and backcasting Reference code: 50139757_2 Commencement date: August 1st, 2025 Work location: Hamburg Application deadline: 04.06.2025 The Climate Service Center
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will
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Knowledge and experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl
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. The research program may also involve a numerical simulation component. Your tasks #analyzing measurements of ocean turbulence using autonomous glider vehicles #use and develop machine learning methods
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: The successful candidate will work in a team developing metal-free magnetism in graphene-related materials and organic 2-dimentional crystals by means of theoretical materials science. A wide range of quantum
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. Applicants should have experience with tissue culture and standard molecular biology methods. Basic knowledge of computer programming (using the R software environment) and hands-on experience working with
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: 01.10.2025 Application deadline: 03.09.2025 Tasks Execution of experimental work in a mouse model of cortical multiple sclerosis Application of in vivo imaging and quantitative analysis methods Investigation
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environment On-campus mentoring and “soft skills” training programs Prospective applicants should prepare the following documents: CV (up to 2 pages) detailing academic training and achievements List
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at investigating spin-crossover (SCO) complexes in direct contact with surfaces, where the interaction of the molecules with a metal substrate is gradually tuned by inserting an insulating layer of variable