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Field
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computational workflows on a high-performance cluster. You will test hypotheses using data from multiple sources, refining your approach as needed. The role also involves close collaboration with colleagues
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experience with R (possibly also Stata, Python or other statistical software) Excellent oral and written English-language communication skills Established record of population health research and scholarship
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electronics. Experience in C/C++, Matlab, and Python programming. Experience in power systems software like PSCAD, PSS/e. Preferred Qualifications: Experience in real-time simulation hardware like Opal-RT and
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cluster. The successful candidate will conduct research under multiple Eratosthenes Centre of Excellence projects funded by the European Union, including the flagship Excelsior H2020 Teaming project (https
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Finland. Position in one or multiple of our research groups with highly diverse and international researchers Competitive salary: Postdoc: 4100 - 4300 Euro/month before taxes, depending on experience
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multiple opportunities to train graduate and undergraduate students, collaborate with other labs testing our GEVIs, and compete for early career fellowships. Consideration will also be given to exceptional
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)—for multiple vehicle types (light vehicles, vans, buses, coaches, and trucks). Users can customize data to reflect specific use cases, supporting more tailored and informed assessments. While VMF supports unit
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‐sharing agreements. Data analysis & interpretation: Process and interpret high‑throughput sequencing outputs to assess biodiversity patterns across multiple MPA sites. Comparative evaluation: Critically
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 1 month ago
(https://airbornescience.nasa.gov/mms) has a long history of providing research-quality, in situ meteorological measurements on multiple platforms as part of over 30 airborne missions since the 1980s. We
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in Python, with experience in PyTorch, TensorFlow, and common ML libraries (e.g., scikit-learn, pandas). Publication record in peer-reviewed journals or top-tier AI/NLP/ML conferences. Strong