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Computer Science, Artificial Intelligence, Machine Learning, or a related field; Strong background in image processing, computer vision, and deep learning; Excellent programming skills (Python preferred), with
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architectures in the field of computer vision and with training, validating and inference processes in machine learning; Familiarity with generative AI; Curious about mathematics and biology; Excellent
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the Department of Machine Learning and Neural Computing (MLNC) at the Donders Centre for Cognition (DCCN) and the School of Artificial Intelligence (AI), both part of the Faculty of Social Sciences of Radboud
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Social and Everyday explainability; application of Machine Learning (such as Reinforcement Learning), Symbolic AI techniques (such as formal systems), or NLP techniques, in Human-AI collaboration; Human
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self-sovereign identity, yet it remains unclear whether these two visions—centered on different loci of sovereignty—are fully compatible or whether they will ultimately serve the same ethical and
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of biomechanical modeling, image segmentation, vision-based motion capture, machine learning, and control systems. Experience with OpenSim model creation and simulation. Keep in mind that this describes
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projects in the marine domain. We envision that you harness the power of AI, specifically in computer vision applications, as well as sensor technology to enhance fisheries, aquaculture and ecological
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some academic research experience post-Master level. Demonstrable affinity with archival sources. Strong skills in GIS-based research, additional experience with computer vision and machine learning is a
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of extreme weather events and land use change on vegetation seasonality and efficiency. For this, you will develop and apply geo-artificial intelligence methods, including spatiotemporal machine learning