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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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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
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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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programming skills in Python or C++, and practical experience with deep learning libraries (e.g., PyTorch) Desirable criteria 1. Research experience in one or more of the following areas: tactile sensing
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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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open to candidates with a strong interest in either: i) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane
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France 91120, France [map ] Subject Areas: Applied Mathematics - statistical learning, deep learning, AI for mathematics, AI for Science Appl Deadline: 2026/03/24 03:59 AM UnitedKingdomTime (posted 2026
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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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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA