154 computer-security "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dip" "Dip" positions at Forschungszentrum Jülich
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only
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results Your Profile: A Masters degree with a strong academic background in physics, mathematics, computer science, or a related field Proficiency in at least one programming language (Python, C
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REMUNERATION: We will pay you a reasonable remuneration for your thesis In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits We welcome
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partner is possible Unique HDS-LEE graduate school program (including data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/ Qualification that is highly welcome in
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holidays and weekends (e.g. between Christmas and New Year) Further development of your personal strengths, e.g. through an extensive range of training courses; a structured program of continuing education
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Scientific communication of the results (publications, conference presentations) Intense interaction with consortium Your Profile: Master and PhD degree in materials science, physics, chemistry, informatics
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structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de
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Your Job: JuPedSim https://jupedsim.org is a platform for simulating people flows, developed in C++ with a Python API and a C interface to SUMO https://eclipse.dev/sumo/ . A React-based web
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environment, we offer you much more: https://go.fzj.de/benefits We welcome applications from people of diverse backgrounds, in terms of age, gender, disability, sexual orientation/identity as
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high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel computing. Together with our existing rich dataset, we will inform a soil-plant digital twin, enabling