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Field
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of 5G Non-Terrestrial Networks (NTN) and/or satellite communications, as well as data acquisition and data analysis Proven programming experience, particularly in Python, combined with a willingness to
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the opportunity to participate in our TALENTA program: a comprehensive career and development program that includes individually tailored qualification offers. You can find out more here. Want more? Then apply and
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semester plan adjustments, Opportunity to write a study or final thesis. The position is temporary. However, we are interested in long-term collaboration. The monthly working hours amount to 20–30 hours
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university degree in a mathematics program, you have an above-average doctorate and a habilitation or comparable achievements. You have experience in acquiring third-party funding. You are willing to teach
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Expertise in multi-omic data analysis is essential Previous experience in single-cell genomic data analysis is advantageous Expertise in programming and statistical analysis, ideally in Python, R and/or other
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. This position investigates how modernization can instead be made spec-driven, traceable, and verifiable, combining deterministic program analysis with agentic AI components. The work is grounded in real legacy
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the JLU’s Center for Applied Computer and Data Science (ZAD). Duties: You will take responsibility for teaching within the faculty’s bachelor’s and master’s degree programs, which also include students from
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professional experience in a research environment (e. g., academic research group, core facility, company) Strong programming and data analysis skills, with demonstrated expertise in Python and scientific
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development; Regulatory pathways for medical devices Programming experience in MATLAB, Python, LabVIEW, or related environments. Strong analytical and problem-solving skills. Excellent communication skills and
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manipulators is beneficial Strong programming skills in Python, C++ and experience with ROS (Robot Operating System) Experience with machine learning frameworks in particular PyTorch and reinforcement learning