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with the centre’s user partner Kongsberg Satellite Services (KSAT). We are therefore seeking someone with a strong interest and competence in deep learning. Working environment: The project will be done
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courses in computing and related areas, with preference for candidates who can teach in one of the following areas: AI and Machine learning (courses like Applied Machine Learning, Deep Learning
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assistant, and have expertise to teach foundational AI courses such as introduction to AI, machine learning, deep learning, and large language models, as well as advanced AI courses aligned with
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School, College and University committees, and may also lead a discipline area or strategic learning and teaching portfolio. To be successful in this position, you’ll have: PhD or demonstrated equivalence
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those related to climate change, forest health, and sustainability. Teach undergraduate and graduate courses that appeal to students across natural resources and forest management. Advise and mentor
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funds, facilities and resources which includes things like PhD scholarships, seed funding and research spaces. Collaborative approach: Engage with staff and HDR candidates across all relevant teams
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closely related quantitative discipline. Demonstrated experience with large-scale deep learning models and modern ML frameworks (e.g., PyTorch, JAX, Transformers), including training, fine-tuning
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spatial biology. Please include a cover letter with your application detailing your qualifications and experience for this position. Describe a deep learning project you have executed—ideally a creative use
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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. Project background We are excited to announce an interdisciplinary PhD opportunity focused on mechanochemical processes driving radical formation and redox cycling in the deep subsurface, with implications