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relevant if there is a strong focus on data-driven modeling, machine learning, and control. In any case, a documented background or experience in control is required. Your education must correspond to a five
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. Any appointment is conditional upon submission of documentation confirming completion of the PhD degree. solid programming skills applied to machine learning algorithms, interactive systems, audio and
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four years are expected to acquire basic pedagogical competency during their fellowship period within the duty component of 25 %. Project description and work tasks Particle accelerators are engines
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an advantage if you have Interest in the mechanical behavior of materials. Experience with machine learning and/or programming/coding. Experience with finite element modeling from civil, mechanical, or marine
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optimization (WOB, RPM, flow rate, etc.) using machine learning techniques Anomaly detection for downhole vibrations, bit failure, and circulation losses Integrating physical modeling, digital twins, and data
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topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant
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shelf lives, and additionally may change colour, texture, and stiffness rapidly. Further, the lack of standardised 3D models for the wide variety of products makes offline learning challenging. As a
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modeling; performing source separation on commercial recordings and extracting audio features (onsets, pitch, harmony, dynamics); curating datasets; and integrating machine learning approaches to complement
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will utilize economic theory, simulation, economic evaluation and machine learning to quantify the benefits of advanced diagnostic technologies in reducing overdiagnosis. Competence You must have
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) for general criteria for the position. Preferred selection criteria Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C