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dissecting RNA structure ensembles in living cells. Particularly, you will: Develop novel methods (both wet and computational) for the accurate interrogation of RNA 2D and 3D structures in living cells. Apply
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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growing need to help instructors develop critical AI literacy, so they can understand both the opportunities and limitations of these tools. It is also essential to support them in creating inclusive
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will develop and carry out the project under the supervision of Dr. Senka Neuman Stanivukovic, Dr. Ksenia Robbe, and Dr. Florian Lippert. Together, they bring expertise in environmental humanities
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-research-projects-awarded-through-open-competition-domain-science-m-programme ). Responsibilities and tasks: Development of novel electrocatalysts for N2 fixation. Fabrication of single cells with targeted
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Organisation Job description As a researcher, you will play a key role in developing and implementing the DNPP’s research programme. Your responsibilities will include conducting independent and
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-time position (1.0 FTE). A holiday allowance of 8% gross annual income. An 8.3% end-of-the-year allowance. The successful candidate will first be offered a temporary position of one year with the option
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Doctoral Network addresses these challenges by developing a neuromorphic platform that is inherently self-aware in terms of energy consumption, secure operation, and system reliability. As part of
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Doctoral Network addresses these challenges by developing a neuromorphic platform that is inherently self-aware in terms of energy consumption, secure operation, and system reliability. As part of
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Doctoral Network addresses these challenges by developing a neuromorphic platform that is inherently self-aware in terms of energy consumption, secure operation, and system reliability. As part of