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Field
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responsibilities for the Research Assistants are: Learn psychophysiology data management and processing methods Implement data management, preparation, and processing methods with participant files with detail
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sustainable heritage management decisions (particularly in an African context), using advanced methods in satellite imagery analysis, remote sensing and machine learning, combined with geospatial analysis
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engineering, data science, or related fields Strong programming skills (especially in Python), and experience with simulation, modelling, data analysis, machine learning, and hardware control Solid
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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research techniques; or an equivalent combination of education and experience. Basic computer skills (Microsoft Office Products, Canva) Desired Qualifications Experience working with children Excellent
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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) Developing machine-learning based exoskeleton controllers to work across tasks 2) Designing and validating new robotic lower-limb prostheses 3) Exploring other high-risk high-reward research areas related
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GNSS, wireless navigation and machine learning is desirable Additional skills that are highly appreciated in this position are the willingness to work hard and to learn more about the fascinating
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted