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Field
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: Using big data insights to optimise the manufacturing process The second phase of this project will focus on processing and utilising machine-learning techniques to analyse large volumes of data from
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are comfortable working with Git and API tooling, such as Postman. You have experience in machine learning, NLP/LLMs, multimodal systems, computer vision, or scraping. Having experience in data science
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well as access to sequencing facilities, high-end computer clusters, and an imaging and electron microscopy core facility. Research in our group covers diverse invertebrate lineages, with particular strengths in
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to develop and implement machine learning/deep learning tools for personalized medicine in cancer by exploiting electronic medical records and medical images in relation to cancer diagnosis and the
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) Theoretical knowledge and practical experience in artificial intelligence-driven techniques for image processing Excellent proficiency of oral and written English in a scientific context Meriting criteria
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manufacturing. It is meritorious to have previous experience in data analysis and processing with Python (or similar), preferably including documented experience with machine learning tools. It is meritorious
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biomedical image processing. Within the scope of machine learning and computer vision, there will be freedom to suggest your own research directions, and to become acquainted with new techniques and approaches
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electroluminescence and photoluminescence imaging, preferably daylight and field-based methods. Proven skills in data analysis, image processing and machine learning. Experience with PV performance modelling, power
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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Mathematics, Computer Vision, or Data Science. -Knowledge of statistical inference methods and machine learning. -Experience in spectroscopy and imaging is an asset. -Strong programming skills in Python