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Field
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learning, foundation models, including large language models, agentic AI, multi-agent AI systems, transfer learning, self-supervised learning, and federated learning. The Postdoctoral Researcher will be
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work with a multidisciplinary team to advance agentic AI tools for simulation, interpretation, data analysis, and scientific discovery. The appointment is expected to last two years and the contract is
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more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world
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members. As the projects mature and evolve, the incumbent will also participate in the development of molecular probes as potential diagnostic agents for use in preclinical rodent models for drug discovery
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PostDocs on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks
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, and multimodal modeling of protein function and cellular state. We seek candidates with strong technical preparation in modern AI/ML, a demonstrated record of scholarly achievement, and experience in
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trial design, dose-finding methods, phase I/II trial designs, model-assisted or model-based trial designs, or oncology clinical trials. Prior publications or preprints in biostatistics, statistics
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agents. The Postdoctoral Associate will work as part of a highly integrated research team utilizing a variety of laboratory technologies and approaches. Specialized skills in molecular/cellular biology and
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SciLifeLab. The successful candidate will work closely with Avlant Nilsson cancer cell modeling lab and Wei Ouyang’s AICell lab who together will develop the first foundation cell model based on HPA and other
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++, or JavaScript Experience with AI or machine learning techniques, including large language models or agentic systems Experience developing or integrating interactive visualization systems, including web-based