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Field
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schools. PhD students are expected to collaborate closely with their supervisors and will produce a PhD based on four papers of a publishable standard. PhD students also acquire teaching experience by
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learning) to empirical data. Statistical analysis. Writing research articles for publication in peer-reviewed journals. Presenting findings at conferences and research seminars. WE REQUEST Professional
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including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here . Applications received after the deadline will not be considered. All
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experimentally verified results is a necessary: Research Topics: Learning-Based Autonomous Systems for Field Robotics Reinforcement learning-based navigation Multi-agent coordination and communication-aware
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for transport You hold, or will shortly acquire, a Master’s degree in a related discipline. Ability and passion for operating at the intersection of different fields, particularly transport engineering and
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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, or a closely related field Strong programming skills, e.g., Python, and familiarity with machine learning and/or software engineering workflows; experience with Git and empirical evaluation Experience
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development opportunities, including a membership to Academic Impressions, LinkedIn Learning, and UT Dallas Bright Leaders Program. Visit https://hr.utdallas.edu/employees/benefits/ for more information
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application to FWO or another external funding organization (depending on your profile and in consultation with your PhD supervisors). Education You will provide support with teaching and also teach high
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tuition assistance and reimbursement Commuter benefits, discounts and campus perks Learn more about these and additional benefits on our Benefits & Wellbeing Page . EEO/Non-Discrimination Commitment