122 parallel-processing-bioinformatics Fellowship positions at Nanyang Technological University
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. Experience in AI system security, adversarial robustness, and resilience of autonomous agents in emerging technologies and computer security (e.g., malware analysis, bug detection, program repair). Excellent
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Responsibilities: To develop process for cooling high-power electronics To fabricate device prototypes of such cooling device Conduct research and experiments at the MAE laboratory facilities Supervise and guide PhD
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, process modelling, data processing and reporting, progress report preparation, etc. Journal paper publication. Assistance in research proposal preparation, etc. Assistance in mentoring junior students
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, or a related field Extensive experience in the controllable synthesis of nanomaterials. Expertise in machine learning-assisted materials design and process optimization. Expertise in
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the following tasks: Development of new large language models for process modeling Development of new decision-making algorithms Provide regular project updates to principal investigator and funding agency Report
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for energy and catalyst, AI-assisted material design/screening, and the green flow processes/reactor design and understanding. For more details, please view https://www.ntu.edu.sg/mse/research. We are looking
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and materials processing methods for solid cooling devices. The candidate will play a crucial role in solid state chemistry synthesis, vacuum sealing, processing, characterization, property evaluation
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data collection with stakeholders from participating schools, organize, and manage data storage (f) Process and conduct qualitative data analysis (i.e., transcribing, coding) (g) Write reports, be
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and development in the chemical conversion and valorization of hazardous waste streams, focusing on complex, reactive materials. Involves process design, optimization, and scale-up, including reaction
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the environmental performance of established and emerging processes. This role includes designing and conducting cradle-to-gate and cradle-to-grave LCAs, building life cycle inventory (LCI) datasets, and integrating