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Field
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machine learning, data science or engineering, with two years of clinical experience. Successful candidates will have strong clinical background and an interest in population level data analytics
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(Machine Learned Potentials) type approaches, and/or multi-objective approaches. - in-depth knowledge of Python programming languages (or C++, Fortran) and the Unix system; - Certified level in written and
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biological science and civilian mapping agency, USGS collects, monitors, analyzes, and provides science about natural resource conditions, issues, and problems. Research Project: The USGS Eastern Ecological
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: Developing and implementing digital twin platforms and graphical user interfaces (GUIs) to support construction monitoring and decision-making. The role involves leveraging machine learning and data
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machine learning in order to release this information and make it available for scientists and conservation biologists around the world. The project aims to accelerate the identification and ecological
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(segmentation analysis by machine learning) and automatic language processing on large quantities of digitised historical photographs and their metadata. - management, enrichment and structuring of project data
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are also expected. Profile PhD in Data Science, Computer Science, Mechatronics, Remote Sensing, Engineering Geology or other related discipline Demonstrated expertise in machine learning and computer vision
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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and choose suitable machine learning methods and monitor soil health. Your key responsibilities will include the following: Collaboration: Work closely with researchers from Australia and India partners
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-to-end lifecycle management of machine learning models, encompassing model development, testing, deployment, monitoring, and continuous improvement. Academic experience in the field of crop improvement