33 software-engineering-model-driven-engineering-phd-position-"https:" Postdoctoral positions
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 12 days ago
the energy transition Experience and competency in quantitative analysis, including applied econometrics Experience in applied theoretical modeling a plus Proficient at statistical software, e.g., STATA
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and Engineering. About us You will join the Healthy AI Lab , a research group led by Associate Professor Fredrik Johansson , that develops machine learning methods and theory to improve data-driven
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POSTDOCTORAL SCHOLAR - AI IMAGING/RETINAL DISEASE Requisition # 4555 Position Basics Advertising Ends on:Thursday, March 12th, 2026 Advertising Started on:Thursday, February 26th, 2026
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presenting technical work at scientific conferences Required Qualifications: Required qualifications PhD in engineering, neuroscience, biostatistics, bioinformatics, computer science, or a related quantitative
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discipline. Preference will be given to applications submitted by candidates within five years of receiving their PhD. A PhD in an engineering domain and solid knowledge of space systems are required for this position
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Qualifications Educational Requirements PhD in engineering or science (e.g. Physics, etc.) majors with a focus on NDE, advanced manufacturing, sensing, robotics, or AI/ML Required Experience Academic
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highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud
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learning, multivariate modeling, or data-driven approaches, as well as interest or experience in the integration of neuroimaging with genetic or transcriptomic data, is considered a strong merit. Prior
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the team. Each position will address a complementary research area within the project: 1. Quantum Control and Reinforcement Learning (CINN, Asturias) Develop AI-driven control strategies based
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methodologies and technologies; developing novel methods, new technology implementations (such as ML models) and high-level products; contributing to the development and curation of open data sets and tools