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environment. Key research Objectives: AI Innovation (Taxonomic Identification): Developing and optimizing deep-learning architectures (e.g., YOLO) for the automated detection and classification of nocturnal
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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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integration (up to 3 million cells) using deep learning-based approaches, hierarchical clustering, and cell type annotation benchmarked against published CRC atlases Deconvolution and TME characterization
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. Melodia, SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control, IEEE INFOCOM 2026 [3] A. Duttagupta, M. Jabbari, C. Fiandrino, M. Fiore, J. Widmer, SymbXRL: Symbolic
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
well as strong presentation and publication skills Desirable requirements: Experience in machine learning / deep learning (e.g., PINNs and neural‑operator methods such as DeepONet, FNO) In addition you have: A
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learning-powered algorithms as well as hybrid approaches, combining either reinforcement learning or deep learning (Graph Neural Networks) with human-based modelling, for fully flawless and autonomous method
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critical thinking, creativity, strong written/verbal communication, strong interpersonal skills, be able to coordinate between people from diverse fields, and have enthusiasm for learning and mastering new
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science or biostatistics Ability to work independently and collaboratively Meritorious qualifications: Experience with machine learning or deep learning Experience with computer vision or image analysis Experience with 3D