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Field
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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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, especially large language models (LLMs), enable the development of interactive AI agents that can support human learning in complex, safety-critical environments. Human-centered AI in this project means
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goals, generate task plans, and work safely and effectively alongside human operators in industrial settings. Recent advances in large language models (LLMs) and agent-based AI systems offer unprecedented
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robots in case of terrain changes—by processing incoming sensor and IoT data on-the-fly. Natural Language Processing (NLP) and Large Language Models (LLMs) bring a complementary human-centric dimension
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environment Interest in biological questions and disease mechanisms Basic understanding of opportunities and limitations of LLMs and transformer models Experience with genomics or transcriptomics, next
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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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currently in a unique position of benefiting from the hindsight of billions of dollars spent in research and development in large-language models (LLMs). This second-mover advantage position creates
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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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) and Reinforcement Learning (RL) to acquire manipulation skills and conduct dexterous grasping. PhD candidate will explore the use of Large Language Models (LLMs) to guide task understanding, planning