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, and publication list Two educational or professional recommendations A copy of an abstract or reprint of an article All documents must be in English or include an official English translation. Connect
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approaches. Familiar with common scientific programming languages such as python, C++, or Fortran. Experience with high performance computing environments is welcome. Candidates who do not strictly meet these
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Ulla Schildt/NHM 19th April 2026 Languages English English English Natural History Museum PhD Research Fellow in Systematic Botany Apply for this job See advertisement About the position A 4-year
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English. Interest and experience in research in the field of spatial economics will be valued. Preference will be given to candidates with experience in dealing with survey data. WORK PLAN The Fellowship
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of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects
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language model (LLM)-based techniques to improve program analysis and vulnerability assessment. Support system integration, benchmarking, deployment, and technical documentation. Job Requirements: A PhD in
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interpreting Next Generation Sequencing (NGS) and genomic data. Experience with shell scripting (Bash), and at least one high-level programming language (Python or R) is essential. A sound knowledge of soil
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 11 hours ago
science databases. This data analysis may be performed using various programming languages such as, SAS, R, or Python. As part of this opportunity, you will be able to engage with a multi-disciplinary
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- Ph.D. in chemistry, physics, or a closely aligned field. - Demonstrated experience in conducting quantum scattering calculations. - Strong programming skills in languages such as Python or C/C
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experience in wireless communication systems as well as familiarity with design and training of state-of-the-art large generative AI models, e.g. Diffusion Models, Multimodal Large Language Models, etc