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: https://www.jobbnorge.no/en/available-jobs/job/294558/phd-research-fellow-in-deep-learning-for-imaging-of-marine-ecosystems Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/294558/phd
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engineering or similar. Knowledge and experience with deep learning models applied in computer vision. Remarkable academic trajectory, validated by a strong record of publications in relevant international
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communication skills. Proficiency in developing deep learning models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in
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Europe Marie Skłodowska-Curie Actions Doctoral Network (MSCA DN) COMBINE. The successful candidate will undertake research on: Deep learning for solidification in multiphase flows with radiative heat
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learning, and Chinese history and culture. Ideal candidates should possess strong programming skills and solid understanding of AI methodologies, along with a deep engagement with humanistic inquiry and its
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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. The Department of Geosciences is one of eight departments in the Faculty of Engineering. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/298154/phd-candidate-in-hard-roc… Requirements
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 16 hours ago
more advanced concepts such as tools for supervised/ unsupervised learning that will be helpful for deep learning focused courses. Estimated course enrolment: 35 Estimated TA support: 1 Class schedule
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
applicant will contribute to the AIGLE project by: · Developing innovative scientific Deep Learning/Machine Learning algorithms for flash flood forecasting. · Contributing to the collection