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information, or quantum algorithms Knowledge of machine learning (e.g., deep learning, optimization, or learning theory) Programming skills in Python and experience with scientific computing (e.g., NumPy, SciPy
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interdisciplinary environment spanning explainable AI, causality, knowledge representation, and neural networks. Research (90%) research in probabilistic machine learning and neuro-symbolic AI (e.g. neural nets
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15 Apr 2026 Job Information Organisation/Company Graz University of Technology Department Institute of Machine Learning and Neural Computation Research Field Computer science Researcher Profile
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independently, yet in collaborative fashion with diverse teams. Excellent customer support skills. Ability and willingness to learn new skills and master new technologies. Working knowledge of learning management
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degree and or its equivalent. 2-4 years of related experience. Extensive experience using Macs and Windows machines. Knowledge of educational technology, research in the learning sciences, and data
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resources. Teaches, monitors and advises students while they practice within the machining lab. Performs student learning outcome assessments to make continuous improvements in learning outcomes. Actively
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the consideration of transfer learning, machine learning interaction potentials, and existing knowledge from experimental studies. Techniques/methods in use: Density Functional Theory, Machine Learning Applicant
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
, leveraging on data analytics and machine learning to improve learning outcomes and engagement in the classroom, and Development of personal GPT-powered AI tutors that use the knowledge from (1) to provide
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of computational neuroscience concepts - Expertise in dynamical systems theory - Knowledge of machine learning - Experience with neural data analysis ### Technical Skills - Advanced scientific programming (Python
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related field. 0-2 years of industry, research, or applied project experience in data science or machine learning. Experience gained through internships, co-ops, academic research, or applied capstone