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Field
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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includes the opportunity for three weeks of training in higher education teaching and learning. The postdoctoral fellow will: Develop and maintain harmonized satellite time-series datasets (Landsat and
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). Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Knowledge of basic molecular biology
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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technologies. The candidate will work at the intersection of Artificial Intelligence, Scientific Machine Learning, and Energy Materials, contributing to the design of self-driving laboratories and no-code
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photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of experimental apparatus. Experience with vacuum
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spectral based sensing, including Ultrasound and Hyperspectral Imaging (HSI), Artificial Intelligence (AI) and Tiny Machine Learning (TinyML). Duties As a Postdoctoral researcher you are expected to perform