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) within the Department of Computer and Information Science . At STIMA we conduct research and education in both statistics and machine learning, at the undergraduate, advanced and PhD levels. We regularly
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use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for microscopy image analysis are especially relevant. Include a link
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” research themes. The successful candidate will have: a PhD in Translation Studies/Machine Translation; practical experience conducting data-driven research in a machine translation/large language models (LLM
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testing and collaboration with infrastructure owners or managers - Experience in supervision - Knowledge of data-driven methods, signal processing, or machine learning - Familiarity with sustainable
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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data
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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation
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identity, and gender expression), marital family, and parental status, pregnancy, genetic information, military enlistment, or veteran status, and any other class of individuals protected from discrimination
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to the successful completion of a PhD degree. For more information see: http://www.mn.uio.no/english/research/phd/ What are we looking for in you? Personal skills: We are seeking a highly motivated, enthusiastic and
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information please visit: https://www.brunel.ac.uk/about/our-history/home We are seeking to appoint a Research Assistant who will work on the ELOQUENCE European project which aims to study Large Language models
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. Describe a deep learning project you have executed—ideally a creative use of a vision transformer, U-Net architecture, or Diffusion model that you trained yourself. Projects in computer vision for microscopy