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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural language
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machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency in Python and relevant
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Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer
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interpretation of remote sensing data, allowing for rapid decision-making in critical situations, such as during natural disasters. AI models can process big datasets efficiently, helping to make informed
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during natural disasters. AI models can process big datasets efficiently, helping to make informed, unbiased decisions, and ensuring resources are distributed to those most in need. Additionally, when
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multispectral/hyperspectral data processing. Proficiency in programming languages such as Python or R for data analysis and processing. Excellent communication skills and the ability to work effectively in a
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remote sensing and image analysis for soil fertility mapping. Experience with data fusion techniques and multispectral/hyperspectral data processing. Proficiency in programming languages such as Python
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optimization techniques. Proficiency in programming languages such as Python or R for data processing, data analysis and model development. Hands-on experience with the use of hydrological and snow models (e.g
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applications. Experience with coupled/hybrid modeling frameworks, as well as parameter optimization techniques. Proficiency in programming languages such as Python or R for data processing, data analysis and