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isolation; electron microscopy and nanoparticle tracking analysis for vesicle characterization; RNA isolation and PCR; SDS-PAGE-based protein analysis; cell culture and immunostimulation experiments; and flow
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methodological framework in the core areas of each track of the programme (economics, finance, marketing and accounting), followed by courses designed to expose students to the research frontier in a variety of
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Max Planck Institute for Solid State Research, Stuttgart | Stuttgart, Baden W rttemberg | Germany | about 1 month ago
are a high level of commitment, basic knowledge of solid-state science and a good knowledge of English. Candidates with an outstanding BSc degree are eligible to apply for a fast-track PhD. Details
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 22 days ago
other excellent research facilities (EEG, eye-tracking, fMRI-TMS). The PhD student will receive generous support for professional travel and research needs (~2500€/year). Additionally, the student will
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an MSc or equivalent degree in biology, psychology, medicine, physics, or related fields. Exceptionally qualified students holding a Bachelor's degree can apply for the PhD fast-track programme. German
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BAM Bundesanstalt für Materialforschung und -prüfung | Berlin, Berlin | Germany | about 2 months ago
the reliability of non-destructive testing and structural health monitoring methods The objective is to develop and validate a hybrid approach combining simulation and experiment to analyse the reliability
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research. The specific focus of the Ph.D. project will be tailored to the candidate’s skills and interests and will align with the objectives of the aforementioned consortia. A key component of the work will
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three connectivity levels (none, medium, high) to test spatial dynamics of RD. Your Responsibilities Create a Personal Career Development Plan (PCDP) outlining research objectives, timelines, skill
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state-of-the-art control techniques for particle accelerators, this PhD project has a clear practical objective: to define the optimal SRF cavity control approach for the HDC upgrade of EuXFEL, currently
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on massive datasets that exceed the capabilities of single-node systems. A key objective is the development of efficient iterative algorithms for training networks based on random features, which can be