61 component-labeling-cuda PhD positions at Delft University of Technology (TU Delft) in Netherlands
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. Your work environment You will carry out this research as part of the ERC-funded RIPPLE project, led by Professor Giacomo Marangoni, and embedded within the Policy Analysis section of the Multi-Actor
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, terminals, shipping companies, and other port actors for this important challenge. Your research will be part of the PortCall.Zero project - a five-year project with multiple industry partners such as Port
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laboratory. The results will be shared globally as part of the Task 12 on PV Sustainability in the International Energy Agencies (IEA) Photovoltaic Power Systems Programme (PVPS) to increase the impact and
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final thesis. For more details please check the Graduate Schools Admission Requirements . Please note: You can apply online. We will not process applications sent by email and/or post. As part of
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the world. Code Review Efficacy The reviewing track will explore ways to make code reviewing more effective and efficient. A major element is how LLM tools relate to reviewing, either when code is produced by
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applications sent by email and/or post. As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer
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on the design and fabrication of electron-optical components, which depending on the final design, may be realized using MEMS technology, conventional precision machining, or a combination of both. The final
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project PolarRobustness, you will explore this question as part of a team. Your role will be to investigate the changes in the physical properties of the cell polarity process after the loss of key polarity
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climate resilient policies! Job description This 4-year fully funded PhD position is part of the ERC Consolidator project “Systemic physical climate risk in complex adaptive economies” (SPHINX). The SPHINX
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? No Offer Description Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15