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with a strong academic background in Machine learning, Natural Language Processing, and/or Software Development. Applicants with an experience in one or more of the above academic fields are encouraged
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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Mathematics, or a related field A strong background in image/signal processing, particularly in computer vision. Strong programming skills and experience with at least one deep learning framework e.g
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical, IoT systems. Key Responsibilities Participate
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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semimetals, magnetic monopole fluids, quantum spin liquids and exciton fluids. • Data Management, Analysis & Machine Learning Knowledge and experience in high volume image-array data acquisition and management
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Python and R; - Demonstrable experience with Machine Learning; - Excellent problem-solving skills and the ability to work both independently and as part of a team. This position is for full-time, on-site
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. Additional optional skills and qualifications: Experience in computer vision for intelligent processing of documents. Contracting requirements: Presentation of the academic qualifications and/or diplomas