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- and LLM-enabled methods and tools to structure, harmonise, and analyse clinical data in a FAIR, privacy-preserving, and clinically meaningful manner, with particular attention to unstructured and
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data scientists, software engineers, biomedical researchers, and clinicians. Your research will focus on developing AI- and LLM-enabled methods and tools to structure, harmonise, and analyse clinical
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PhD in Computer Science, AI, Machine Learning or related field Experience Strong track record of publications in top-tier venues (e.g. CORE A*) Expertise in reinforcement learning, AI agents, and LLM
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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-level layer implementations - extend hardware developments to use near-FPGA DDR and HBM memories - create functional demos using networks of interest (Yolo, Resnets, LLMs, ...) - create proof-of-concept
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, sentiment analysis and artificial intelligence (particularly LLM) API integration Familiarity with grant writing and funding acquisition is desirable. Benefits Eligibility YES FLSA Status Exempt Apply now
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candidate has practical experience working with LLMs and demonstrate the actual work completed. We offer a generous annual leave package plus discretionary University closure days, excellent training and
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an advantage: Natural Language Processing; LLMs; R; Python. Experience with teaching and supervision will be considered an advantage. Very good skills in Norwegian or other Scandinavian language is an advantage