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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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the dispersion of these macroplastic items in these flow fields; comparing the results of the simulations to results from an experimental campaign of floating trackers; collaborating with a postdoc and two PhD
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perception systems, using deep learning and simulation-to-real domain adaptation techniques. You will work with a multidisciplinary team, contributing to fundamental and applied research. Your role will
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the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the … Where to apply Website https://www.academictransfer.com/en/jobs/356464/postdoc-deep-learning-based
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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competencies Education You should have completed within the past five years or be close to completing a PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science
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risk of cardiovascular disease. Your main tasks and responsibilities are: developing deep learning-based model for detection and quantification of coronary calcifications in contrast-enhanced CT images
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on resilient 6G connectivity for mission-critical applications, and contribute to innovative European and Dutch national projects together with a team of talented PhD researchers? Information The future 6G
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expertise in deep learning including experience with libraries such as Pytorch Have strong communication, presentation and writing skills; Enjoy working in a multidisciplinary research environment; Are highly