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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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: Microbiome; Bacteria; Microbiology; Metabolites; Nuclear Magnetic Resonance, Mass-spectrometry, Chemometrics; Multivariate statistics; Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL
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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 July 2026. Faculty members
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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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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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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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learning and genomics. Specifically, the candidate will work on developing and applying cutting edge deep learning frameworks for modeling massive single cell and bulk multi-omic datasets. Duties include
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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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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