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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 2 months agomechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights
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Medicine Postdoc Appointment Term: Fixed term for one year, with opportunity for renewal Appointment Start Date: September 2026 Group or Departmental Website: https://med.stanford.edu/neonatology.html(link
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and the Department of Sociology. Participate in biweekly Families, Lifecourse and AGeing research cluster meetings. Where to apply Website https://www.academictransfer.com/en/jobs/362662/postdoc
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developing interactive web applications or scientific software. • Experience or interest in dryland, rangeland, or plant ecology. • Strong machine learning background and enthusiasm for applying deep learning
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packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in
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and other generative systems). The ideal candidate brings a strong machine learning foundation, curiosity about sound and music computing, and enthusiasm for collaborating with PhD students and postdocs
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practical knowledge of statistical and programming packages is mandatory, including R, Python, and/or SQL. Extensive experience with advanced statistical techniques and algorithms, such as Machine Learning
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-quality datasets that are structured and annotated for simulation and machine learning. Develop Digital Twins and simulation pipelines for virtual validation and predictive performance assessment. Implement
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, solvent-based recycling process for complex plastic waste streams such as multilayer packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process