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informed decision-making grounded on a sustainable and responsible use of resources in manufacturing. Vacancy number 15918 Key responsibilities As part of this Postdoc position, you will be contributing
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We are seeking a Postdoctoral Research Assistant in Cellular Neuroscience to work in the group of Professor Colin Akerman in the Department of Pharmacology. This is a fixed-term post, for six months
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analysis, (ii) collaboration and teamwork, (iii) gender inequality, (iv) diversity, (v) online controlled experiments, and (vi) network science. The ideal candidate is self-motivated and hard-working with a
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
. Responsibilities for this position includes:* Developing deep-learning approaches for insight and analysis about mouse behavior from video * Machine Learning: use your expertise in statistics and machine learning
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cell lines and primary cells. Technical support in fluorescent microscopy acquisition. Managing Image processing and analysis of live imaging microscopy acquisitions. Basic requirements of the position
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plasticity metrics derived from functional MRI data. Investigate developmental differences in infant brain functional networks. Support generation and testing of improvements for code bases for the analysis
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an innovative academic education to more than 20000 students, conduct pioneering scientific research and play an important service-providing role in society. We are one of the largest, most international and most
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team, analysing and interpreting spatial transcriptomic data. You will work on the Colorectal Cancer - Stratification of Therapies through Adaptive Responses (CRC-STARS) programme, developing and
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their team. The work will be jointly supervised by Professor Helen Byrne (Mathematical Institute, MI), Professor Simon Leedham (NDM), Dr Joshua Moore (MI) and Dr Eoghan Mulholland (NDM). Colorectal Cancer
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language models for bacterial genome analysis. Train and evaluate models on large-scale bacterial genomic datasets. Apply the developed models to address specific research questions, such as gene function