301 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions in Denmark
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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candidate is expected to hold: A master degree in biomedical engineering or computer science, Excellent programming skills (Python). Experience with data curation, large-scale datasets, and machine learning
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Department of Electrical and Computer Engineering (ECE), Aarhus University (AU) invites applications for a position as Tenure Track Assistant Professor/Associate Professor in electronics
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environment. You can communicate in English and Danish. Experience with the manufacturing of parts by CNC milling is a plus. Experience with CAD design software such as NX is a plus; willingness to learn
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and maintenance of monitoring buoys and related sensor systems. Apply image analysis and machine learning techniques to ecological datasets. Develop and implement multi-platform monitoring frameworks
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initiatives Commitment and ability to teach and supervise students at bachelor’s and master’s levels, including course development in digital design, computer architecture, and AI hardware Strong communication
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are looking for a student to support the operations of the Cyber-Physical Lab, a facility where physical experiments are directly interfaced with and controlled by computer systems. The position requires a
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have: A relevant PhD degree (e.g., NLP, AI, ML, Security, Cryptography, or a related field) A relevant MSc degree (e.g., Computer Science, Software Engineering, Machine Learning, Artificial Intelligence
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characteristics: be self-motivated, having a can-do attitude, willing to learn be able to program in C++ and/or Python be at least familiar with ROS2 and Linux have decent understanding of robotics
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials