11 msc-in-statistical-learning PhD positions at Delft University of Technology (TU Delft)
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the Faculty of Mechanical Engineering of Delft University of Technology. More information on the team and research can be found on https://peirlincklab.com . Job requirements You have an MSc degree in aerospace
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optimizations and approaches inspired by machine learning within the framework of cognitive radar; and C) verify the developed approaches with suitable simulations and experimental demonstrations. Specifically
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hold a relevant MSc degree in applied geoscience and engineering, with background on geology and reservoir simulation. Applications from MSc holders of related engineering domains will be certainly
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to aerospace engineering. More than 200 science staff, around 270 PhD candidates and close to 3000 BSc and MSc students apply aerospace engineering disciplines to address the global societal challenges
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on simulated yet realistic data. We expect to publish 3-4 journal papers on this topic as well as a number of conference papers. Job requirements An MSc degree in an engineering discipline relevant to the PhD
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to develop state-of-the-art scanning-NV microscopy. A MSc degree in physics and fluency in English are required. TU Delft (Delft University of Technology) Delft University of Technology is built on strong
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, refine the process model for scale-up, and assess the structure's performance under varying pressure differences. Requirements We are looking for candidates that meet the following criteria: Hold a MSc
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Delft explores AI-driven methods to enhance closed-loop simulation for safety-critical scenarios. A key focus is developing learned simulators that generate radar and lidar data from camera sensors
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strong academic record with a solid background in Machine Learning (Deep Learning, generative models, diffusion models). Knowledge in sensor data processing and radaris a plus. Good programming skills
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the power of data and machine learning! Job Description We are seeking a highly motivated PhD candidate to join our research team focused on Collaborative Metadata Management for Large Data Repositories