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, experience managing and analysing ancestrally diverse genomic datasets, and expertise in computational genetics, including variant annotation and genome-scale analyses. Proficiency in R or Python, excellent
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of the following: Python, C++, or Java - Successful experience developing software in either Windows or Linux environments - Experience with version control software such as Git - Willing to travel ~10% to remote
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Engineering, or a related discipline. Proficiency in Java and/or Python programming. Familiarity with software development tools and platforms (e.g., IDEs, cloud environments etc). Demonstrated ability
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MATLAB or Python and practical skill with both; and strong oral and written English communication skills. This appointment is funded jointly by The Fu Foundation School of Engineering & Applied Science and
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(or equivalent experience) in computer science, electrical engineering, civil engineering, mechanical engineering, data science, artificial intelligence, or a related field • Strong Python programming skills • 0-2
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(for example, R, Python, or Matlab). Experience with graph modeling, Bayesian statistics, or causal inference is a plus. The candidate will join an integrated team of computational scientists, molecular
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computer science, electrical engineering, civil engineering, mechanical engineering, data science, artificial intelligence, or a related field • Strong Python programming skills • 0-2 years practical software
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Machine learning/AI based classifiers Proficiency in coding using R and Python and other similar languages High level analytical capability Ability to communicate complex information clearly Informal
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Qualifications Strong background in electrical engineering and computer engineering. Hands-on experience in computer networking and embedded systems. Proficiency in programming using Python and C/C++ Proficiency
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: Python (Django), Java (Spring), TypeScript (React), Docker, Terraform, GCP. We maintain a DevOps culture and use Scrum for our day to day work. We have also adopted an "open by default" approach to new