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position will work with researchers on the DASS programme to develop and promote freely available software, implementing new statistical methods and algorithms for identifying anomalous structure in stream
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, coordination, and management of information and data, development and implementation of computer models and simulations, development of research materials (figures, presentations, papers, etc.) and work
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), debuggers, code verifiers and unit test frameworks and gain experience in graphical user interface design and algorithm development. Posting end date: July 11, 2025 Number of positions (est): One (1) position
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, the Software Engineer Associate will work alongside researchers to develop and optimize software solutions that support our lab, ranging from large-scale data processing pipelines to computer vision algorithms for animal
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algorithms for organoid characterization Design and execute experiments independently, troubleshoot technical issues, and determine optimal methodologies based on literature and past experience Prepare data
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. The position is for one year, renewable subject to satisfactory performance. Successful candidates will conduct research and develop advanced deep learning and computer vision algorithms. Candidates are expected
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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experimental neuroscience aimed at advancing our understanding of memory and its disruption in aging and disease. AREAS OF RECRUITMENT Computational Neuroscience We seek exceptional scientists developing
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This project aims to develop outcome measures for early detection of neurodegenerative disorders using artificial intelligence (AI) and various sensing modalities, offering personalised support to
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across domains. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data