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opportunity to be part of the project “Bavarian Artificial Intelligence for Insect Monitoring” (https://go.ur.de/baimo ), which investigates the effects of climate change on insect populations as part of
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opportunity to be part of the BaySensAI project (https://go.ur.de/baysenseai ) and develop an AI-based prediction and monitoring system for biodiversity data as part of the Bavarian research network bayklif2
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at the department. The main supervisor will be Prof. Dr. Kathy Lüdge from TU Ilmenau. Where to apply Website https://jobundkarriere.tu-ilmenau.de/jobposting/80cd5964173be4cf2a6646500503798… Requirements Research
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with fault detection, system reliability, or fault-mitigation techniques. Strong programming skills (C/C++, Python; hardware-description languages “e.g., HLS, VHDL” is a plus). Motivation to pursue a PhD
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, particularly during measurement campaigns at synchrotron and neutron facilities. Experience with programming languages such as Python is advantageous. Practical laboratory skills and an enthusiasm
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independent work and effective collaboration, particularly during measurement campaigns at synchrotron and neutron facilities. Experience with programming languages such as Python is advantageous. Participation
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international research sites Your Profile: Completed master’s degree in mechanical engineering, material science, physics or a related field Experience in modelling and simulation (e.g. Python) Willingness and
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infrastructure, excellent international networking, and a collegial working atmosphere • Working in one of Germany's most popular cities All details can be found here Where to apply Website https://www.uni
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: TUEILSY-PHD20240930-SAS) Safe Collaborative Manipulation in Cluttered and Dynamic Environments (ID: TUEILSY-PHD20240930-SCMM) A more detailed topic description can be found at https://www.ce.cit.tum.de/lsy
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skills (Python, R, Java, …) and interest to work in polyglot software environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods