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at McGill University), do not apply through this Career Site. Login to your McGill Workday account and apply to this posting using the Find Jobs report (type Find Jobs in the search bar). Position summary: We
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contribute to the excellence of our academic community. We are looking for a postdoctoral researcher with expertise in Bayesian hierarchical spatio-temporal statistics and measurement error methods for a 3
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streams for reliable AI model training. Design of digital twins for process monitoring, fault diagnosis, and predictive maintenance in chemical plants. Key Responsibilities: Create and implement hybrid AI
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for real-time battery health monitoring and fault detection. Collaborate with embedded systems and hardware engineering teams to integrate AI models into the BMS. Optimize AI/ML pipelines for resource
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) positions to start in September 2026 or earlier. Outstanding candidates in quantum information theory, quantum simulation, lattice gauge theory, fundamental physics, and quantum error correction, broadly
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sequencing (DArTseq). This position will analyze genetic structure, assess species identification, and detect hybrids. Morphological data for identification, SWD infestation rates, and seed viability
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candidates which are explored in more depth. In particular you will work on the extension, development and analysis of new quantum algorithms for near-term and fault tolerant quantum computers for drug
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capacity to co-supervise research students on projects in this area, with assistance and mentoring from senior academic staff familiarity with the theoretical methods of quantum error correction, and the
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the Joensuu or Kuopio campus. The position will be filled for a fixed term of two years starting on 1 April 2026 (or as agreed). Please find more information below and submit your application no later than 29
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position, you will lead the development of a probabilistic, error-aware surrogate model capable of delivering fast, uncertainty-quantified predictions for complex multiscale–multiphysics processes in OFPV