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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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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 10 hours ago
mathematical background. - Familiarity with deep learning frameworks such as PyTorch. - Commitment, team working and a critical mind. - Fluent verbal and written communication skills in English
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microfluidics, nano-electronics, nano-biomaterials, big data, and deep learning. Applicants must hold an M.D., Ph.D., or equivalent degree and have extensive postdoctoral experience, along with a strong
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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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Science Programs, and MS in Computer and Information Science (https://cse.aua.am/ ) invite applications for a full-time faculty position in Machine Learning at the rank of Assistant Professor, starting in
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Training Group Information (JTT) https://arxiv.org/pdf/2107.09044 [3] Hacohen, Weinshall (2019). On the Power of Curriculum Learning in Training Deep Networks http://proceedings.mlr.press/v97/hacohen19a
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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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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with
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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