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Field
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localization algorithms, spearhead energy-aware navigation planning systems development, and oversee real world validation of underwater navigation technologies while mentoring junior team members. Key
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learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor crash predictions
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safety and computational social choice and develops formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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Localization and Perception System based on Multi-sensor Fusion Job Description: Responsible for development of robust and reliable sensor fusion algorithms for localization and navigation algorithms of robot in
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trajectory optimization, nonlinear programming, or genetic algorithms. High-performance computing (HPC), parallel numerical workflows, or GPU-accelerated model execution. Terms of Appointment This is a full
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research, computer science, or a related field. The appointment is typically for one year and may be extended, subjected to performance and availability of research funding. Key Responsibilities: Conduct
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This position focuses on advancing the mathematical and computational foundations of interpretable algorithms for dynamical systems and explainable AI. The successful candidate will develop new
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Dunedin and will be supervised by Associate Professor Colin Fox. The successful applicant will play a leading role in developing scalable Bayesian algorithms, probabilistic models, and computational methods
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group in Statistics and Data Science at the Department of Mathematics at University of Oslo, in close collaboration with the machine learning group at the Department of Informatics. About Integreat See