11 data-"https:"-"https:"-"AMADE-research-group" Postdoctoral positions at Virginia Tech
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. Opportunities are provided for diverse career development activities, including: • Skill Development: Acquiring new research techniques (e.g., AI/ML, data assimilation). • Networking: Establishing a strong
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modeling approaches. • Investigate interactions between food products and paper-based packaging materials across diverse product categories. • Collect, process, and analyze experimental data, including
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specialization in Data Science/Applied Econometrics and its application in applied economics and social sciences broadly defined. The position is one-year, 12-month, calendar-year appointment, with the possibility
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fingerprinting and forensics in multi-source watersheds. The Postdoctoral Associate will contribute to project design and lead laboratory work, data analysis and modelling in collaboration with the OWML PFAS
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methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems. Candidates
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in areas related to quantum information theory, quantum error correction, and machine learning. The position will start in fall 2026 and will be for two years, with a possible 1-year extension pending
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and distribution systems, including unit load design and supply chain efficiency. • Collect, process, and analyze experimental data from mechanical testing equipment; prepare technical reports and
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{lPayScaleID} Overtime Status Exempt: Not eligible for overtime Appointment Type Restricted Salary Information Commensurate with experience Hours per week 40 Review Date November 30, 2025 Additional Information
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, fluorescence in situ hybridization, and bioinformatics. Preferred Qualifications Molecular biology and genetics laboratory experience; web computer skills; ability to learn new techniques and procedures
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systems. The individual will be responsible for: • Develop and implement models for the structural and mechanical performance and optimization of mass timber systems, using data-driven approaches