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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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postdoctoral researcher to join our dynamic animal science and artificial intelligence team. The candidate will develop novel algorithms that advance the use of computer vision in precision livestock farming
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Is the Job related to staff position within a Research Infrastructure? No Offer Description We are seeking an ambitious candidate to develop Machine Learning models and frameworks for time series
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research strand 3 «Addressing key methodological challenges». The positions are tied to CREATE’s strand 3. Strand 3 aims to develop novel methods and statistical software that are tailor-made to the research
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data from public or commercial databases and develop algorithms using existing libraries. Based on the previously identified resources, the Ph. D. thesis will then focus on the extraction of oxides and
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the opportunity to conduct independent research and originate research topics over time Contribute to the long-term success of our research-driven algorithmic trading business Below is a list of skills and
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and Algorithms group. The post holder will contribute to the intellectual life of the School through conducting and publishing outstanding quality research, engaging in high quality teaching, and
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independent research and develop novel algorithms. You have strong analytical and problem-solving skills. You have a research-oriented mindset and motivation to work at the intersection of AI and communication
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alternative ways of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How