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techniques, methods, and research, especially deep learning literature, and how these methods apply to our use cases Ability to manage multiple projects and assignments with a high level of autonomy and
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scientific computing. You are proficient in several languages (Python, C/C++, or Fortran), with extensive knowledge in AI/ML and parallel programming (GPU, multi-threading, etc.). You have strong software
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governance solutions in medium to large organizations across multiple business domains. Hands-on experience in Collibra administration, including workflow management, data catalog integration, and overall
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that ensuring a prosperous global future depends on the ability to support local people and communities everywhere. By working in and across multiple scientific areas, CALS can address challenges and
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for integrated sensing and communication (ISAC). Responsibilities: Lead the design, setup, and execution of ISAC channel measurement campaigns across multiple environments, hardware platforms, and frequency bands
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tethered to an autonomous ground vehicle. Multiple vehicles operating together will create high resolution maps of emissions and airflow, at unprecedented spatial resolutions. The project will be carried out
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to the body of knowledge by enhancing the safety, relia- bility, and efficiency of automated vehicles by developing a collaborative multimodal perception system. This system leverages data from multiple sources
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, creating new variables, checking data consistency, reshaping data and joining multiple datasets. Should have experience with, or education in, building the following models: generalized linear regression
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related field. Documented expertise in machine learning and time-series modelling (e.g. LSTM, XGBoost, CNN). Strong programming skills in languages such as Python and R. Experience with phenotyping data
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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and