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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
, leveraging on data analytics and machine learning to improve learning outcomes and engagement in the classroom, and Development of personal GPT-powered AI tutors that use the knowledge from (1) to provide
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Union within the project “A Comprehensive Trustworthy Framework for Connected Machine Learning and Secure Interconnected AI Solutions (CoEvolution)”, - CUP F23C24000210006 – selection code: ipd_10D_0426_09/IINF
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Electrical Engineering, Computer Science, or a related discipline. A research-oriented attitude. Solid background in machine learning and optimization methods. Knowledge and experience in (wireless
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. Fluency in English (C1 level or higher). Hands-on experience with Python and AI/ML frameworks (e.g. PyTorch, TensorFlow, HuggingFace). Solid foundation in machine learning, deep learning, and neural network
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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Do you want to be part of introducing "new space" and setting an imprint on future satellites? If you have interests in physics, technology, climate and machine learning, this is the doctoral
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of advanced testbeds to the implementation of antenna structures. We are now looking for a new colleague in the form of a doctoral student in Electrical Engineering with focus on secure distributed learning
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governments and institutions to achieve outcomes traditional approaches cannot. Job Summary The Bike Shop is hiring a Machine Learning Researcher. The Bike Shop is a CS and Economics research lab focused
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 days ago
in environmental or territorial studies. Preference will be given to candidates with aptitude for working in disaster risk management environments supported with GIS and Machine Learning, basic
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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful