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reinforcement learning for robotics applications. Hands-on familiarity with robotic manipulators and motion planning. Proficiency in deep learning frameworks such as PyTorch or TensorFlow. Experience with ROS
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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environment to study these topics given its expertise in Machine and Deep Learning, Computer Vision, Signal Processing, and Multimedia. Also, its declared vision to work especially in presence of imperfect data
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development. Job Details The successful candidates are expected to: • Conduct research in computer vision and deep learning, including literature review, hypothesis formulation, and experimental design
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architectures for (bio)medical research and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based
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, performance management, and developing high‑performing teams. Deep understanding of finance and accounting frameworks, and the ability to break down terminology and illustrate how policies, internal controls
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established chair Evolutionary Diversity and Biogeography. The aim of this chair is to explore the evolutionary patterns in plant-species diversity in space and (deep) time to gain insights into the origins
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical
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Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning Applied domains: Machine Learning for Cybersecurity, AI for 3D Imaging, Recommender
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 3 months ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning