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Field
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statistical analyses on large databases Designing and running experiments Structural modelling and related econometrics Proving theoretical results Basic Qualifications Bachelor’s degree by start date (required
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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are supported with recommendations on how to best improve their sustainability achievements; Leading and working in project teams that vary in size and complexity. These may vary from about five colleagues
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MEng degree (or equivalent) and a PhD in Maritime Engineering and Technology or pertinent disciplines (Res Assistant if no PhD), adequate knowledge of modelling marine engines operations with alternative
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. Integrate independent references for rigorous validation. Methods & Data Engineering Design generalizable, well-documented pipelines for data fusion and modeling using established geospatial and scientific
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to adapt to multidisciplinary environments and a focus on professional growth and development. Desirable requirements Good knowledge of quantitative methods (econometric models, time series econometric and
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technology for the forthcoming generation of highly energy-efficient computing systems. FeFETs are programmable, non-volatile silicon devices that enable innovative architectures to efficiently execute complex
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 16 hours ago
efficacy studies in relevant mouse models. These studies will expose the successful candidate to cutting-edge prime editor engineering approaches and the delivery of these reagents in vivo via AAV or lipid
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in