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Technology (IST) has 44 full-time faculty members with research foci in cybersecurity, data mining and machine learning, natural language processing, human-centered computing, and computing and engineering
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services, a specialist degree in instructional technology, and PhD degrees in marine science: coastal and marine systems science and education sciences. We seek a highly motivated individual with a
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engineering Technology » Marine technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 9 Apr 2026 - 23:59 (Europe/Oslo) Country Norway Type of Contract Temporary
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for a full-time, on-site PhD position in machine learning, forecasting and time series analysis. Reykjavik University, Department of Engineering. Duration: 3 years. Start date: Negotiable. Reykjavik
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence
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university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique
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have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and spoken proficiency in (scientific) English is
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, technical depth, and a strong track record of applied research in Computational Biology, Structural Biology, Protein Engineering, Machine Learning, or a closely related field. Strong understanding and
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related to staff position within a Research Infrastructure? No Offer Description Aalto University is where science and art meet technology and business. We shape a sustainable future by making research
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 11 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward