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researcher who enjoys developing methods for complex biological data and working closely with experimental scientists. You meet the following criteria: a PhD, or a PhD close to completion, in machine learning
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of electrolyzer technologies, digital twins, model order reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration
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Constructor Technology, invites applications for a PhD position in machine learning for software engineering and formal methods, on the Constructor Fabric project. Constructor Fabric turns a company's informal
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the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
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machine-learning strategies for multimodal representation learning and for the integration of the complex biological datasets generated within the consortium. Duration and start date: 48 months, full-time
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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to teaching, clinical evaluation, mentorship, scholarship, service, and academic-practice partnerships that support the Family Nurse Practitioner program and broader College of Nursing priorities. Teach
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literature. Main responsibilities • Design, implement and optimize signal-processing, statistical and machine-learning algorithms for eye-movement (oculomotor) and voice/speech data. • Validate
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. For example, investigating costs and benefits related to the adoption of state-of-the-art technologies that permit tracking and chain of custody. The team will also explore machine learning and artificial
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leave and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Position Description Advertised Job Summary Senior