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Field
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on the development of AI-supported and human-in-the-loop methods for automating and optimizing software instrumentation processes. Drawing on a strong background in software engineering, data analytics, and machine
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–robot interaction and ergonomics Automation and robotised workplaces (industry use-cases) Field robotics and harsh-environment robotics Predictive maintenance, sensor systems, multimodal monitoring AI/ML
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bioinformatics including NGS (Nanopore, Illumina, PacBio) Experience with automation and coding in Python or other programing languages Experience with protein software tools like AlphaFold3, Boltz2, PyMOL
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Initiatives in Forest Research (WIFORCE) program. The successful applicant will work on the development of bioacoustic monitoring methods using automated recording units (ARUs), deep learning methods, and
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10.01.2022, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at Technical University Munich invites applications for the position of a Research Assistant (m/f/d) for performing fundamental research in the frame of the project DrawOn funded by the Bavarian...
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farm management through innovative sensing technologies, advanced modeling, and intelligent automation. This PhD project contributes to PhenoRob’s initiative to develop digital twins, i.e., high-fidelity
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of the simulations, that will allow the incorporation of these data into the AI materials development and discovery workflows. Contribute to the development and application of the Automated Characterization Platform
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printing, embedded sensing, and adaptive control, aiming to meet the unique challenges of automation in life science environments. Research tasks and qualifications The main purpose of this PhD position is
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Systems (ADAS) and Automated Vehicles (AVs) beyond traditional camera, radar, and lidar sensors. Unlike these sensors, which require direct line-of-sight, acoustic detection captures traffic sounds like
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- Specialised education programmes in key capacity areas; AI-DAPT – 101135826 – AI-Ops Framework for Automated, Intelligent and Reliable Data/AI Pipelines Lifecycle with Humans-in-the-Loop and Coupling of Hybrid