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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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coordinating the research collaboration between the Africa Business School and the Growth Lab at Harvard Kennedy School. The primary focus of this position will involve data collection and analysis using
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parameters to optimize recovery and product quality; Evaluate potential valorization routes for process by-products and residues; Analyze, interpret, and synthesize experimental data to support process
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), and assembly of drone airframes. Testing: Wind tunnel, fatigue, and flight tests for mechanical and aerodynamic validation. Data Processing: Documentation, data analysis, and contribution to reports
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be an effective team player to interact constructively with the interdisciplinary team of the CBS department and other collaborators with different backgrounds. For further information, please contact
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The successful candidate will contribute to the following research axes: AI-driven territorial diagnostics and foresight, integrating multi-source satellite data with machine learning and spatial modeling Climate
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-based data collection and integration, fieldwork, mineralogical and geochemical characterization (gypsum, anhydrite, native sulfur). Ability to engage professionally with a diverse population of faculty
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and analysis of experimental data; Take the lead on writing of research reports and communicating the scientific results for conferences and scientific publications Participate in the training and
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Participate in field campaigns across Africa Publish in high-impact journals Contribute to data visualization platforms Required Qualifications Ph.D. in Atmospheric Science, Environmental Science, Remote
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analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission management platforms, contributing to the deployment of smart UAV demonstrators under