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Field
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/admittance, force control Experience with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g
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applicants who combine strong quantitative skills with a deep interest in the fundamental mechanisms shaping plant communities and their response to climate change. Qualifications: • PhD in Ecology or a
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the UK. A flexible, supportive and inclusive team environment at a research-intensive university, where your work is seen and has meaning. Opportunities to collaborate, learn and utilise resources from a
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flows, or reinforcement learning-based design optimization. Strong programming skills in Python with experience in PyTorch, JAX, or equivalent deep learning frameworks. Ability to work independently
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of materials. We are searching for a Postdoctoral Researcher with excellent numerical skills and a deep understanding of condensed-matter physics concepts. Experience in machine learning, DFT, symmetry aware
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) analysis • Research, development and implementation of deep-learning approaches • Network architecture search • Real-time image analysis • Establishing multi modal (video, thermography, acoustic, RFID
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environments (Gazebo, Unreal Engine, or Unity). You have experience in artificial intelligence (Deep Learning, PyTorch) or embedded systems (ROS2, FPGA/VHDL design). You are curious, show scientific rigor and
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. Significant experience of developing deep learning methods using computational frameworks such as PyTorch, TensorFlow etc. Experience of working with molecular questions in the biosciences and applying AI
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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Relativity, and cosmological measurements using GWs such as Hubble constant and probes of inflation and phase transitions in the early universe. We are developing new data analysis methods like the use of deep