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candidate will support experimental work related to biogas production, biomass characterization, and process optimization. Tasks will include laboratory-scale anaerobic digestion experiments, analytical
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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
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our teaching and research in designing and optimizing biology-based solutions as future sustainable food and ingredient technologies. UCPH is strengthening this area by offering the new MSc Biosolutions
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FutureFoodS project SpeedyFermHub – which aims to support the development, optimization and scale-up of fermented plant-based food prototypes. Within the project, the Department of Management at Aarhus
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed
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techniques (PINNs, Scientific ML, data-driven optimization, etc) to surpass traditional techniques for discovery, design and optimization of thermal technologies, seeking necessary experimental validation
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trajectory within areas such as social sustainability, spatial justice, diversity, speculative architecture, futures and imagination. Next to architectural design competencies and skills, the candidate has
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NAS that considers accuracy, latency, energy, and carbon at the same time. Delivering a carbon-aware NAS framework that generates Pareto-optimal model architectures and model libraries whose variants
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governs their thermal performance, operational robustness and cost. The overall aim of the project is to design, optimize and digitally model the cooling system of a pilot-scale electrochemical reactor
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed