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Field
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others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and clear written and verbal
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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, Chemistry, Physics, Applied Mathematics, Materials Science, Chemical Engineering, or a related technical field. Demonstrated research experience in machine learning or deep learning for scientific
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for support in technology development. You will work closely with a PhD researcher at the German partner who focuses on the underlying machine learning models, and you will help coordinate the joint work across
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the Computing & Engineering Department. The group is very dynamic, ambitious, well networked and delivers state-of-the-art research in a range of machine learning and data science topics, publishing research
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. Teamwork: Ability to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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meetings, contribute to grant applications, and build an independent research career in cancer immunology and neuro-oncology. Minimum Requirements PhD, MD, MD/PhD, or equivalent doctoral degree in immunology
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logical way and parametric simulation engines. Familiarity of machine learning / AI techniques and custom LLMs generation is beneficial. Knowledge of life cycle assessment (LCA) and LCA methods, carbon