58 coding-"https:"-"FEMTO-ST"-"CSIC" "https:" "https:" "https:" "https:" "https:" positions at Nature Careers
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) in the field of model and code development for the transformation and circularity in energy-intensive industries (steel, cement, and plastics) starting as soon as possible. The position is initially
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our website: https://www.list.lu/ How will you contribute? As part of a major European project on high‑performance, multifunctional textile materials, the Luxembourg Institute of Science and Technology
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. Neural information coding for efficient spikebased image denoising. Technical Report arXiv:2305.11898, arXiv, May 2023a. URL http: //arxiv.org/abs/2305.11898. arXiv:2305.11898 [cs] type: article. A
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or representative code samples Please submit your application online by February 22, 2026 using this link: https://imba.onlyfy.jobs/job/zrqid1ul - Applications received after this date may be considered until
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qualified, those with disabilities or with equivalent status pursuant to the German Social Code IX (SGB IX) will receive priority for employment. Please submit your detailed application with the usual
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in the Environmental Sensing and Modelling (ENVISION) unit
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or with equivalent status pursuant to the German Social Code IX (SGB IX) will receive priority for employment. Please submit your detailed application with the usual documents by March 5, 2026 (stamped
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Renewal BC (FRBC) and by the facilities and equipment of the Quesnel River Research Centre (QRRC). The QRRC (https://www.unbc.ca/quesnel-river-research-centre) is a freshwater-focused field station based in
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econometric methods for analysing large datasets. Are proficient in coding and data management using tools such as Stata, R, or Python. Have a strong interest in the research area and have a strong independent
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for the Generative AI Era (BRIDGE) - exploring approaches to transform unstructured bug reports into actionable insights that connect to code, support diagnosis, and improve the speed and quality of fixes. Application