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of Microsoft Office programs or the demonstrated experience to learn such programs. Additional Information: This is a full time (100% FTE), permanent position. This position is overtime eligible/ineligible
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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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and best practices in all areas of human resource management including recruitment, retention, employee relations, learning and organizational professional development, compensation, benefits management
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with the implementation new systems effectively . Ability to quickly learn and adapt to new software and tools. Preferred Qualifications: Master’s degree or higher. At least one year of experience with
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to teach basic courses in AI (in particular machine learning) for non‐technical audiences, and advanced courses for expert and professional audiences, in particular on generative AI methods, while developing
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an excellent scientific track record. Proven expertise in environmental genomics, metagenomics, or large-scale omics data analysis. Experience with machine learning or AI approaches in biological data is an
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, application of machine learning methods to climate science. Climate models of varying complexities and their possible mitigation strategies. Emergence of extreme events in weather and climate. Research
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navigate complex records and legal requirements. You’ll be comfortable using a variety of software tools and eager to learn new systems that support your work. This role offers ongoing training and
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arrangements to help manage your work-life balance; ongoing learning and development opportunities to grow your career; an inclusive and supportive culture and environment to work in, both online and on campus
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specialising in interdisciplinary topics, for example, candidates specialising in Machine Learning and Data Science, Quantum Computing, Theoretical Computer Science, Financial and Actuarial Mathematics