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research experience, preferably in programming languages, compilers, applied mathematics, and optimization techniques a strong background in compiler, code generation, and machine learning would be
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limited to) molecular and cellular imaging, synthetic biology and computational and machine learning approaches. The predominant criteria for appointment in the University Tenure Line are a major commitment
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cloning, managing repositories, branching, and implementing collaborative and reproducible data analysis workflows. Experience in applying machine learning or AI approaches to genomics data (e.g. BLUP
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Professor, or Full Professor) in the area of data science and artificial intelligence for materials design and innovation. We seek candidates whose research focuses on the development of machine learning
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in medicine and other study programmes and graduate initiatives in which the Faculty of Medicine is involved is expected. Beyond the core research area, the appointee will also teach in all degree
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for therapeutic gain. Specific areas of interest include, but are not limited to: • Machine learning applied to immunological challenges, such as modeling immune responses, antigen specificity, or cellular
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experience in AI teaching We understand AI very broadly – and adequate experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past
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existing SC analysis tool, by integrating machine learning and benchmarking components, thus helping evolve it into a market-ready solution capable of real-time threat intelligence and adaptive vulnerability
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to this search are candidates with expertise ranging from NeuroAI, machine learning, and artificial intelligence to brain-body interactions, including cancer neuro and neurodegenerative disorders, and systems and
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meet the specialized behavioral demands of different animals. More information about the lab and their work can be found by visiting https://sarvestanilab.com/ About the Postdoctoral Scientist role: We