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Expansion: Implementing the next phase of the project to transition from rigid-body models to sophisticated systems for protein ensemble modeling. Computational Optimization: Resolving hardware-specific
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screening and engineering for microbial bioprocess development; microbial physiology and adaptation for industrial applications; high-throughput screening and automation for strain optimization
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development, characterization, and commercialization of 2D amorphous carbon materials for advanced semiconductor and related industrial applications. The candidate will: • Develop and optimize wafer-scale UV
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, process optimization and food characterization. Key Responsibilities Lead bench formulation of mushroom‑based and plant‑based food products, especially focusing on muscle‑like architectures to achieve
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tools for forage producers. The research aims to advance ecological weed management strategies and provide producers with practical information to optimize forage production, livestock performance, and
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, and resource optimization studies to address real-world sustainability challenges. Explore emerging technologies, frameworks, and practices to advance sustainable solutions in industries. Industry
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
of Engineering. Key responsibilities include the design and implementation of novel AI-driven goal-oriented semantic communication strategies, signal processing algorithms, performance optimization methods, and
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, process optimization, and project management. Key Responsibilities Lead and manage applied food processing related research projects in collaboration with Principal Investigators, Co-PIs, and industry
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-polysaccharide interactions under different food processing conditions. Profile phenolic compound and evaluate the bio-functional properties such as antioxidant activity Identify optimal processing conditions that
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approaches. Develop, optimize, and maintain code and algorithms supporting predictive models. Combine computational algorithms with laboratory automation to enable self-driving laboratories and automate