160 evolution "https:" "https:" "https:" "https:" "https:" "https:" "BioData" positions at Forschungszentrum Jülich in Germany
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datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
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the scientists, you will expand our equipment pool, in order to meet user needs and keep it up to standards. This can be achieved either through new purchase, or through the development of new equipment in
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usable in continuum-scale soil and plant-water models Contribute to the development and documentation of reusable, open, and reproducible molecular simulation workflows Collaborate with researchers working
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mentoring for building a career in academia or industry Professional development through JuDocS, including training courses, networking, and structured continuing education ( https://www.fz-juelich.de/en
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well as an arrangement for bridging days (e.g. between Christmas and New Year’s Day) KNOWLEDGE & DEVELOPMENT: Your professional development is important to us – we support you specifically and individually e.g., through
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: Your professional development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS): https
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energy system models based on the institute`s own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE. Your tasks in detail: Implementing geothermal plants with material co-production in
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Your Job: Strategic development of the research field on material availability in global energy system transformations, building on the content of the ERC Starting Grant Independent project
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international project partners, embedded in an ERC-funded project. For deeper insights, please have a look at our institute website: https://www.fz-juelich.de/en/pgi/pgi-7 and the research group in which
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Analyze dynamical states of spiking complex neuron networks with respect to network topology and neuron parameters Development of learning rules considering the strong non-linearities of the neurons