44 master-"https:" "https:" "https:" "https:" "UCL" positions at Delft University of Technology (TU Delft)
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this vacancy? Please apply no later than 14 April 2026 via the application button and upload the following documents: A detailed CV. Cover letter An abstract of the Master thesis (1 page). Copies of MSc
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validate your ideas on real chips alongside an international team; thus, it is also critical for you to be a team player, communicating smoothly with your colleagues. Your main activities will include
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, reproducibility, deployment interfaces). Use AI/ML and statistical methods to detect trends, anomalies, and defects in manufacturing and inspection data (primary focus). Work closely with domain experts to define
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your main responsibility, it offers valuable experience that supports your development and prepares you for future academic or professional roles. Teaching activities will not exceed 20% of your total
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unsolicited services. Where to apply Website https://www.academictransfer.com/en/jobs/359528/postdoc-in-advanced-numerical-a… Requirements Additional Information Website for additional job details https
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website about knowledge security. Please do not contact us for unsolicited services. Where to apply Website https://www.academictransfer.com/en/jobs/359529/phd-position-inspection-and-ass… Requirements
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for this vacancy. This exemption must be obtained from the Ministry of Education, Culture and Science (OCW) before an employment contract is agreed upon. Click here for more … Where to apply Website https
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on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on … Where to apply Website https://www.academictransfer.com/en/jobs/359466
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expected to be able to act independently as well as to collaborate effectively with members of a larger team and supervise Master students. Good English skills are required. The work takes place in Delft
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint