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Field
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Post Doctoral Researcher in Human-Centered AI for Software Engineering, Department of Electrical ...
particular with Large Language Models. Solid programming expertise in Python. Be the main author in at least one journal publication in the area of AI4SE, published at a high impact journal. Experience with
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; supporting program and policy evaluation; developing or refining methodologies for enhancements of different aspects of Weatherization Assistant Program (https://weatherization.ornl.gov/softwaredescription
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, experimental, or computational techniques. Experience in applying for research funding. Skilled in analyzing and interpreting data (e.g., using MATLAB, R, Python). Ability to write papers and reports for peer
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optimize and ex-ante evaluate different components of an architecture for improving global public good provision that is sketched here . The architecture consists of two components, tax clubs and reward
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AgriLife and make a difference in the world! Position Information: The successful candidate will contribute to developing and evaluating terrestrial and aquatic nitrogen dynamics and emission modeling, with
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the MET office, the candidate will use outputs from the JULES land surface model to run model experiments that test NBS adaptation potential under different climate change scenarios. For example
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processes for data standardization, data warehousing, quality assurance development and data analysis. A successful candidate will work closely in different aspects of the quality assurance and data
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geologic media As for programming, we prefer familiarity with MATLAB, Python, and C++. Prior experience with high-performance computing (HPC) clusters and Unix operating systems is advantageous. We welcome
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programming in, for example, R or Python. Particularly valuable is a research background in ecology, biodiversity, systems biology, or related areas, as well as experience working with time-series data, dynamic
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning