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Field
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computer science, operations research, applied math, statistics, or a related field Strong background in machine learning, optimization, and/or algorithm design Excellent written and verbal communication skills
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papers, articles, and publications. Assists with the preparation and cleaning of worksite. Initial studies will focus on burn wounds, improving healing, and assessing multiple wound factors using imaging
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. Experience with statistical characterization of data, preferably within a Bayesian framework. Preferred Qualifications: Experience developing a (semi-)independent scientific research program on topics in
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Microsoft Word, Excel, Access and PowerPoint. Preferred Qualifications Proficiency and knowledge of multiple statistical software packages preferred, such as SPSS, SAS, R, Python, etc. Experience with ML/DL
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university partners on systems-level issues. Run advanced quantitative analyses using statistical software. Generate research questions for analysis utilizing quantitative and qualitative data collected. Lead
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, industrial energy systems, energy efficiency of manufacturing industry, or other related fields. You will play a crucial role in the planning, execution, and optimization of our technical assistance program
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for adaptation to sea level rise along the Maine coast. This 18-month grant-funded position will be supported in partnership with Maine Coastal Program (MCP), Maine Geological Survey (MGS), and faculty from
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involves designing and executing experiments, managing multiple projects, and collaborating with research teams. Key responsibilities include conducting field research, optimizing analytical methods
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred
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investigating how targeted interventions modulate brain networks and relate to clinical improvement in PGD. The Fellow will contribute to the development of computational brain models of grief and resilience in