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Field
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include remote sensing algorithm development, modeling studies, data fusion, sensor development, and/or snow satellite mission concept studies. Participation in the design and execution of field campaigns
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following areas: Strong foundation in machine learning, optimization, and deep learning algorithms, including Transformer architectures. Hands-on experience or solid theoretical knowledge of LLMs/SLMs
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knowledge of collaborative software tools, and experience with the implementation of data acquisition or analysis algorithms You have a good track record of published articles in peer reviewed journals
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associate will work both independently and collaboratively to develop and apply novel deep learning algorithms and/or computational chemistry methods for small-molecule drug discovery targeting RNA
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at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim
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(Anthropic, OpenAI). Experience with graph algorithms or NetworkX. Familiarity with adaptive learning or educational technology. Experience with WebAssembly technologies (Pyodide, WebR). OAuth2 authentication
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mechanisms, optimisation algorithms and renewable energy systems WORK-LIFE BALANCE: Optimal conditions for balancing work and private life, as well as a family-friendly company policy. The option for flexible
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. The PhD candidate will join the team Methods and Algorithms for Bioinformatics (MAB). - Supervision: - Dr Eric Rivals, HdR, (LIRMM), CNRS Research Director in computer science and bioinformatics (web https
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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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, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that improves the performance of ROMs, making them more applicable