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Field
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themselves. 2 - Position description The Faculty of Sciences and Bioengineering Sciences, Department Computer Science, Research Group Federated labs AI and Robotics is looking for a PhD-student with a doctoral
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architectures like the transformer neural network, these AI systems predict future health events and adapt to various healthcare tasks, aiming at more accurate prediction of progression of diseases like cancer
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Manchester, CNRS, Thales, AUTh and Eurida. The post is relevant to candidates with computational skills, such as micromagnetic and atomistic simulations, with a strong interest on circuit-level and neural
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: Experience with generative AI deep learning and active involvement in data science and machine learning projects. Experience in neural network architecture, cloud computing, and database development and
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qualified and motivated postdoctoral researcher. The group studies the behavioral, neural, and computational principles behind human learning and decision-making in social environments. Our interdisciplinary
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candidates from all backgrounds to join our community. PhD Researcher: AI-Enhanced Adaptive Design for Dynamic Landscapes School of Arts, Design and Architecture – Department of Architecture Location
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, covering areas such as LLMs, generative AI models, computational biology, and the analysis of high-dimensional neural and behavioral data. The Fellowship is intended for early-stage scientists and will
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computer science, electrical engineering, or related field or be enrolled in a PhD program in these fields. Experienced in one or more ML/AI techniques, such as reinforcement learning, federated learning, natural
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testing and stakeholder engagement. Collaborate closely with the PhD researcher to connect environmental data analysis with computational design innovation. Participate in fieldwork in Norwegian glacier
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for interdisciplinary research and career development. Job candidates must… · Hold a Bachelor’s, Master’s, or PhD in Engineering, Physics, Math or Computer Science, or other Bachelor's plus equivalent relevant