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mathematical modellers and epidemiologists at CERM with strong collaborative links within Singapore and across the wider region. Key Responsibilities: • Collate and analyse data from longitudinal studies
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development and be an attractive and inclusive place to study and work. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) PhD Research Fellow
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forecasting, extreme weather, snow observation, soil moisture data analytics, and reservoir/lake system modeling in support of water resources decision-making. This position will work closely with the NOAA
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research. Applicants for the associate position should have already completed, or be close to completing, a PhD in a relevant discipline. Applicants for the fellow position should also have prior post-doc
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Fellow in Hydrology and Water Resources. The successful candidate will conduct advanced research in hydrologic modeling, flood forecasting, extreme weather, snow observation, soil moisture data analytics
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diverse group of interdisciplinary scientists who develop models of biological systems and work closely with policy makers and stakeholders in the UK and around the world. For more information about SBIDER
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The position is funded by the Open Philanthropy grant “Verifiably Robust Conformal Probes”. The project’s goal is to develop methods for latent probing (aka activation monitoring) of large language models (LLMs
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2 Oct 2025 Job Information Organisation/Company University of Oslo Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 6 days ago
Stage Researcher (R1) Positions PhD Positions Country Portugal Application Deadline 15 Oct 2025 - 00:00 (Europe/Lisbon) Type of Contract Other Type of Contract Extra Information Research fellowship Job
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The position is funded by the Open Philanthropy grant “Verifiably Robust Conformal Probes”. The project’s goal is to develop methods for latent probing (aka activation monitoring) of large language models (LLMs