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on increasingly complex questions where the interplay between different phases is crucial for success. Interface systems—whether involving material transitions, biological systems, energy-conversion devices
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Science. Commitment to undergraduate and graduate education. Demonstrated expertise in machine learning/deep learning and software development (Python; PyTorch/TensorFlow). Peer-reviewed publications and strong
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with fault detection, system reliability, or fault-mitigation techniques. Strong programming skills (C/C++, Python; hardware-description languages “e.g., HLS, VHDL” is a plus). Motivation to pursue a PhD
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-term coevolution experiments to study the evolution of metabolic cooperation Generate and characterize bacterial mutants Perform coculture experiments with different bacterial strains to determine
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. The differences between who we are and what our experiences and ideas are make us stronger. In our mission to work with the best talent, we do all we can to ensure equal opportunities, ignoring aspects such as
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(ECLECTX team). This person occupying this position is planned to work on modeling computing elements, established and emerging, at different levels of abstraction, design and development simulation tools
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: · Developing LCI models and using LCA to assess different scenarios of energy storages in batteries, as well as the realization of preliminary LCAs for novel substances. · Collecting and organising data
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2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant
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consortia of cross-feeding bacteria ” • Generate and characterize bacterial mutants • Perform coculture experiments with different bacterial strains • Analyse the formation of clusters among bacteria using
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charge of: · Developing LCI models and using LCA to assess different scenarios of energy storages in batteries, as well as the realization of preliminary LCAs for novel substances