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-learning models that connect powder characteristics and process parameters with the properties of the final components. These models will support faster feedstock qualification and enable predictive quality
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, Computer Science, Data Science, or a related field Experience with machine learning, data analytics, or computer vision techniques Programming experience in Python and familiarity with machine learning frameworks
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environment for utility mapping technologies. Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in partial automation with limited
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machine learning or computer vision models Technical competencies in one or more of the following: bio-digital systems, biodesign, applied machine learning, computer vision, computational biology, and/or
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and/or the analysis and processing of sensor data, for example building and deploying sensing systems (wearable, ambient, RF, distributed), edge and on-device data processing and machine learning
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that prioritise student learning in the age of AI. Job requirements You hold a PhD in Educational Science, Learning Sciences, Social and Behavioural Sciences, Engineering Education, Human-Computer Interaction or a
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. As a PhD researcher, you will unravel the atomic-scale mechanisms of hydrogen embrittlement in compositionally complex recycled steels, using density functional theory and machine-learned interatomic
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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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at Saxion University of Applied Sciences, you will contribute to the development of machine-learning models that connect powder characteristics and process parameters with the properties of the final
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planning and decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch