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and Drug Delivery Systems for Enhanced Bone Regeneration” (Reference: PROID2025010017). This project focuses on the optimization of additive manufacturing processes (MEX) and scaffold performance
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 5 days ago
: • Experience in the field of oncology. • Knowledge and/or experience in liquid biopsy. • Scientific publications and conference presentations. • Teaching experience. LanguagesENGLISHLevelGood Additional
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research group of Prof Emily Shuckburgh, as well as being part of the Centre for Landscape Regeneration (CLR). The CLR is an ambitious programme of research that aims to provide the knowledge and tools
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new data becomes available, while preserving previously acquired knowledge. A key objective is to design signal representations and learning mechanisms that enable stable adaptation without forgetting
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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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the proteins are extracted. With the emergence of a plethora of new and green methods for protein extraction, limited knowledge exists on how the choice of method affects the extracted protein besides the direct
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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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fellows and students, maintaining laboratory equipment, contributing to in vivo experiments in small animals (mice), and leading optimization studies using existing biological data within the CTRC. Propose
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/Qualifications You will work on: Microbial cultivation using diverse substrates Preparation and characterization of microbial-derived lysates Supporting development and optimization of complex media for mammalian
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11th May 2026 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Hardware-Aware Deep Learning Model Optimization Apply for this job See