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the LLM effectiveness. Key Responsibilities: Research and develop novel ML-based methodologies and algorithms for multilingual and multimodal LLMs. Working closely with other Postdoc/RA/PhD students
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algorithms that can tightly integrate physical hardware, sensors, and computational models. This PhD position is centered on addressing these challenges through innovative computational methods, combining
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to the optimisation algorithms. What we are looking for: Current knowledge in computer science or computational chemistry, physics, or materials science. Current knowledge in AI, ideally for use in the chemical
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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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algorithms, to applications in astronomy and medicine. Multiple positions are open in the context of the £4M research Fellowship TomoGrav awarded by the Royal Society to Dr Kazunori Akiyama (moving from MIT
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associate position to conduct research in machine learning (ML) for applications in High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including
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segmentation algorithms to segment industrial wrapped objects with limited data Develop algorithms to identify the most reliable segmented objects. Develop algorithms to determine the best grasp pose based
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advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data, with a focus on identifying critical transformation windows and assessing phase evolution kinetics
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prediction algorithm and molecular dynamics simulations. For more details, please view https://www.huilingshaogroup.com/. We are looking for a Postdoctoral Research Fellow to design and execute independent and
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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid