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Position Details Position Information Job Title FIN 342 Teaching Assistant Appointment Type Student Employee Job Location Corvallis Position Appointment Percent 100 Appointment Basis 12 Pay Method
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Vacancy ID P020915 Full-time/Part-time Permanent/Time-Limited Full-Time Permanent If time-limited, estimated duration of appointment Hours per week 40 Work Schedule Monday through Friday, 8am-5pm Work
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Position overview Position title: Unit 18 Faculty Salary range: A reasonable estimate for an initial appointment is $70,977. See Unit 18 Lecturer table for the salary range for this position
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance
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work experience in research design, statistical methods and knowledge of computer hardware and software; or an equivalent combination of education and experience. Desired Qualifications Experience
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, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en/department-of-computing-science/ . Project
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Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics
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Advisor must be a strategic thinker with strong communication skills and deep understanding of digital and traditional prospective student communication and marketing methods; they will enhance
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includes, for example, the areas of labour markets, wellbeing and welfare, macro-financing, globalisation and econometrics. The position is specifically at the AI-Econ Lab at Örebro University (https
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FMUP | 1 (ONE) RESEARCH GRANT | ''BEST-COST" PROJECT (REF. HORIZON-HLTH-2022-ENVHLTH-04)'' | Pr. 412
the scope of the project "BEST COST - Burden of disease based methods for estimating the socio-economic cost of environmental stressors" (Ref. HORIZON-HLTH-2022-ENVHLTH-04), funded by the European Commission