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. Proficiency in programming languages like C and Python, as well as deep learning frameworks such as PyTorch and TensorFlow. Knowledge in imaging and computing device and equipment. Strong communication
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journals and conferences such as Journal of Machine Learning Research (JMLR), Transactions on Machine Learning Research (TMLR), NeurIPS, ICML, RLDM, ICLR, IROS, or other top IEEE venues. Experience with
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interfaces under reaction conditions, using machine-learned interatomic potentials (MLIPs) for automatic reaction network exploration for catalyst dynamics, and developing the next generation of kinetic Monte
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both on the sequence and structural level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and
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, cybersecurity, computer networking, machine learning, AI, ERP and cloud computing Conduct research with colleagues. Strong data analytics skills, including the ability to work with large sets of structured and
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large, highly diverse and multi-modal datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet
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researcher to help us deliver it. By combining coherent Raman scattering with machine-learning models trained on plant mutants, the project will shed new light on the cellular-level biochemistry that governs
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are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish
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years of post-qualification experience at the time of application; (b) experience in using machine learning for research projects; and (c) have a good command of both written and spoken English
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems