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PDF, Dev of new porous transport layer architectures for Proton Exchange Membrane Water Electrolysis
with graduate students, technical officers, and machine learning scientists to design, evaluate and intelligently optimize PTLs with innovative structures, delivering design guidelines for next
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eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC
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, green finance, ethical supply chains, and behavioural change. Digital and Technologies: AI and machine learning, cybersecurity, spatial intelligence, robotics, human-computer interaction, and digital
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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility
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(or equivalent) in Computer Science, Machine Learning, Mathematics, or a related technical field. For Postdoctoral Fellows: A completed PhD in one of the fields mentioned above and a strong publication record
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | about 1 month ago
: Electrotechnical and Computer Engineering. Admission requirements: Students enrolled in a PhD. program in Electrotechnical and Computer Engineering, or in related areas, or alternatively, an MSc degree in
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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility
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level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and immunological problem. Improving
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8 Oct 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Biomedical engineering Engineering » Computer engineering Researcher Profile First Stage Researcher (R1
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developing statistical and machine learning approaches for the integration of cancer multi-omics data and the analysis of CRISPR-based screens. Responsibilities include designing bioinformatics workflows