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automation software for high-throughput quantum-hardware preselection and characterisation using image recognition and machine learning tools. As R&D institution we are highly interested to disseminate
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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microscopy images or recorded animal behavior to apply. Such information will aid analysis of experimental data or help establish automated image processing/machine learning pipelines. Technicians will
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the broad landscape of computational science, including artificial intelligence, machine learning, deep learning, and their applications in addressing complex scientific and societal challenges, encompassing
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projects and technical leadership. Basic Qualifications: Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related technical field. Proven experience in
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Nature Careers | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | 11 days ago
Health and the IWK Health Centre in Halifax. The successful candidate will have an opportunity to be part of the Big Data Analytics, AI & Machine Learning research cluster in Computer Science, and the
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in high-performance computing, materials chemistry, theoretical chemistry, molecular dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive
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an outstanding track record or strong interest in the fields of antibody design, structural biology, AI/machine learning, or immuno-oncology, we invite you to apply with a project proposal for a position in our
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clinical research center is a plus; Knowledge and experience of machine learning methods; Constructive attitude, flexibility, outgoing and service oriented; Excellent communication, negociation and
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, and inclusion. The candidate will be expected to teach courses in Ecology and be able to contribute to our introductory general biology course sequences, in particular Biology 111: Unity & Diversity in