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Field
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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of surface chemistry and catalysis, DFT and ML. She/he must be familiar with VSAP software and Python. She/he must be rigorous, curious and able to communicate in English. She/he must be proactive in
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, presentations, software) A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered. Preferred Qualifications: Knowledge of early modern European history
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, document, and release production-quality software packages Department Specific Qualifications Education: PhD in Bioinformatics, Computational Biology, or Computer Science with biological applications
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should possess experience in robotics, drones, UAVs, multi-robot systems or smiliar, and practical applications of LLMs, agents, RAG, MCP, or fine-tuning. Proficiency in software development (Python, C/C
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and collaborators via virtual meeting software (Zoom, Teams, WebEx, etc), phone, and email. Work Environment: Must pass employee health clearance, TB Test and required safety training. Clinical/office
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or software (provide publication and github links). Cover letters are highly desired from applicants. Please address to Dr. Andrew Fiore-Gartland and include a short statement of research interests. The annual
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, acoustic theory and ocean science. Experience with Echoview software is encouraged. The position will be primarily located on the Sharp Campus of the University of Delaware in Lewes, Delaware, however they
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substance use/substance use disorders, HCV, HIV, and homelessness. Proficiency in statistical software (e.g., R, SAS, STATA) and/or qualitative analysis tools (e.g., NVivo, Dedoose). Knowledge of harm
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using secondary data and conducting secondary data analysis in scientific research. Knowledge of databases. Experience in working with various software for conducting survey research and statistical data