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, equivalent to 18 semester hours or equivalent subject matter coursework, CEU’s, vendor, or military; or Possesses an earned bachelor’s degree, from a regionally accredited institution, in information systems
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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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16 Mar 2026 Job Information Organisation/Company Metamind Innovations P.C. Department R&D Research Field Computer science » Computer systems Engineering » Computer engineering Technology » Computer
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of senior researchers, the individual will help apply machine learning methods, with a focus on reinforcement learning, to mathematical problem solving. The role emphasizes hands-on experimentation
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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admissions platform. Where to apply Website https://fenix.tecnico.ulisboa.pt/fenixedu-admissions Requirements Research FieldEngineering » Computer engineeringEducation LevelUndergraduate Skills/Qualifications
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platform. Where to apply Website https://fenix.tecnico.ulisboa.pt/fenixedu-admissions Requirements Research FieldEngineering » Computer engineeringEducation LevelUndergraduate Skills/Qualifications Admission
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– "Fluid gap Electro-Active-Polymer Machines for a New Generation of Mechatronic Systems." The selected candidate will contribute to the development of experimental and theoretical activities aimed
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conferimento di n. 1 incarico di ricerca scientifica di Fascia 1 presso la Sezione di Roma dell’INFN - 12 mesi Where to apply Website https://www.ac.infn.it/ Requirements Additional Information Eligibility
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation