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, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site
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of machine-learning algorithms for unmanned aerial vehicles; dissemination of the results in international conferences and journals; proposal writing for external funds. Your profile The successful candidate
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Are you passionate about solid mechanics and intrigued by machine learning? We are seeking a qualified candidate who can merge expertise from both fields to develop innovative tools for analyzing
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Qualifications: PhD in computer science, mathematics, statistics, or related fields (by the start date). Strong background in stochastic optimization, machine learning, or mathematical statistics. Track record
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, dplyr etc.,) and version control system (git, bitbucket etc.). Experience in use of AI and Machine Learning techniques using Python (sklearn, statsmodels, pandas, numpy) is desirable. Solid and proven
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Natural Language Processing, Machine Learning, or a similar area. Expertise in large language model architectures and training paradigms (transformer models, fine-tuning strategies, RLHF, etc.). Interest in
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intelligence and machine learning for data analysis, scenario design, optimisation, etc.). Candidates are expected to teach and supervise students in our study programmes, particularly the MSc in Engineering
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in one or more programming languages (Python, Java, C/C++). Experience developing or implementing AI applications, particularly using machine learning frameworks (e.g., PyTorch, TensorFlow, Hugging
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, the following qualifications will be advantageous: Published peer-reviewed articles in international journals. Training in statistics and machine learning. Experience with generative AI modeling Bioinformatics
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patterns, and extreme climate events remains a subject of debate. Using a combination of climate modelling, statistical methods, and machine learning, ArcticPush aims to uncover the conditions under which