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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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30 Mar 2026 Job Information Organisation/Company K3Y Ltd Department R&D Research Field Computer science » Computer systems Engineering » Electrical engineering Technology » Computer technology
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*• Experience in Python or another programming language (projects, GitHub repositories, courses, scientific use).• Training or experience in machine learning and data science applied to environmental or energy
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dissemination, and translational opportunities Job Requirements: PhD in Chemistry with a focus on computational/peptide/organic/machine learning or a closely related discipline At least one first-author
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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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engineering Engineering » Mechanical engineering Mathematics » Applied mathematics Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 13 Apr 2026 - 23
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tracking for bioaerosol Applications are invited for a PhD position in the Air Quality and Particle Technology group (Prof. Dr. J. Wang). The successful candidate will be hosted by the Institute
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17 Mar 2026 Job Information Organisation/Company cnrs Department lem3 Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Jun
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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