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on developing new generative modeling approaches, scalable training algorithms, and foundation model technologies. The role is suited for candidates with a strong machine learning background who are excited
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Software Engineer: Keystone Project (Machine-Verified LLM Inference) Mission The Keystone project
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against
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biomedical sciences. We are looking for candidates with A PhD in machine learning, computer science, computational biology, or a closely related field (completed or near completion). Demonstrated experience in
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Experience with machine learning and/or statistical modeling applied to biological data Proven expertise in single-cell data analysis (scRNA-seq and/or scATAC-seq) Interest or experience in multi-modal data
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vision, video understanding, action recognition, multimodal/vision-language models, pose estimation, or large-scale self-supervised learning. Familiarity with neuroscience, or ethology is a plus. Position
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of Singapore, and EPFL (Switzerland). These partners are looking for talents in several domains of machine learning, AI, computational biology, and biology, to develop PhD theses across the main pillars
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physiological acquisition systems; real-time data acquisition; motion capture or positional tracking; Bayesian or hierarchical modeling; reinforcement-learning models; effort-discounting or decision-making models
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, gained through academic research, internships, professional work, or substantial personal projects Experience with machine-learning pipelines, including data preparation, model training or inference, and
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Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field. Exceptional BSc candidates with strong engineering experience will also be considered. Experience in AI and neural