121 computer-science-intern "https:" "https:" "https:" "https:" positions at Forschungszentrum Jülich
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: INTERDISCIPLINARY TRAINING: An interdisciplinary training program, including academic research, international mobility, and industrial immersion INTERNATIONAL COLLABORATION: Collaboration with 32 international
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of data centers Integration of the data basis into an open-source technology database (TechDB) for subsequent internal and external use Familiarization with the national energy system model ETHOS.NESTOR and
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and individually, for example through training opportunities and the structured JuDocS program for doctoral candidates: https://www.fz-juelich.de/en/judocs In addition to exciting tasks and a
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international research contexts Your Profile: Excellent Master’s degree in mechanical engineering, energy systems, computational engineering, or a related field Strong background in numerical methods and applied
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energy-efficient supercomputers Supervise vocational trainees in mathematical-technical software development Your Profile: University degree (Master) in Computer Science, Electrical Engineering, Software
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at international conferences and publish them in peer-reviewed journals Your Profile: A Master’s degree (or equivalent) in chemistry, physics, meteorology, atmospheric sciences, environmental sciences, or related
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the application which project you are specifically interested in. Further details on the projects can be found here: https://www.fz-juelich.de/en/jcns/careers/fellowships/tasso-springer-fellowship-program Your
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an international, interdisciplinary research team Familiarity with Bayesian thinking is desirable No prior biological experience is required; curiosity for life science questions and willingness to collaborate with
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for doctoral candidates (JuDocS): https://go.fzj.de/JuDocs SUPPORT FOR INTERNATIONAL EMPLOYEES: Our International Advisory Service makes it easier for international employees to get started FAIR REMUNERATION
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models of structural disorder based on the periodic average structure, crystal chemistry, and complementary information Generation of a dataset to train ML models Simulation of the corresponding