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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 11 days ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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30th April 2026 Languages English Norsk Bokmål English English PhD Fellow in Machine Learning Apply for this job See advertisement About us The Nansen Center is a Norwegian environmental research
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stability analysis and control, machine learning, dimensionality reduction and high-performance computing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR3346-NADMAA-159/Default.aspx
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Frontiers of the Future grant. CARINA stands for Combining Machine Learning with Forefront Astronomical Instrumentation to Probe Intermediate Mass Star Formation. CARINA is focussed on developing case
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fully funded PhD position in the area of safe data-driven system identification for cyber-physical systems, offered by our research group at the intersection of control theory, machine learning, and
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 13 days ago
the communication and storage needed to retain most of the information. Environment. The PhD will take place at Inria Grenoble, in the Thoth team. This is a large team focused on machine learning, and
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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be found on our lab website: https://www.derosierelab.com/ Salary and benefits: ~€2300 net per month for candidates with less than 2 years post-PhD experience; €2450+ net per month for candidates with
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Information Eligibility criteria Scientific and technical skills: • PhD in applied physics, optics, astrophysics, control systems, or artificial intelligence. • Strong background in machine learning
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, and machine learning methods. Using regression analysis and vector autoregression (VAR) models, the study examines the relationship between macroeconomic variables and the performance of various