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looking for a full-time (100%) doctoral scholarship holder in the field of machine learning for circular polyurethane design. Position You will actively work on the preparation and defence of a PhD thesis
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of inverse photonic design, machine-learning (adjoint or topology optimization), or other advancing design methodologies. Ability to design and model active photonic devices (MEMS, electro-optic or thermo
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electrical engineering, control engineering, applied mathematics, computer science, or a related field A strong background in probability and statistics, machine learning, or control theory Interest in cyber
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://www.academictransfer.com/en/jobs/362880/phd-position-on-critical-approa… Requirements Specific Requirements You have a Master's degree in Industrial Design, Interaction Design, Human-Computer Interaction, or a closely
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health. Successful candidates may have experience in electron or X-ray microscopy, image analysis, AI and machine learning, quantitative data science or computational modelling. They will be able to work
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language processing, large language models, machine learning, network analysis, social media analytics, and large-scale analysis of online discourse and communities. This PhD scholarship will be based within
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heterogeneity govern transport dynamics and degradation mechanisms during extended operation. A coupled mechanical–transport framework, accelerated through machine-learning surrogate models trained on multiscale
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FieldComputer scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Strong foundations in Machine Learning and Deep Learning Excellent Python programming skills Experience with PyTorch
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. This position provides an excellent opportunity for a recent PhD graduate interested in clinical research, tobacco regulatory science, public health, biomedical engineering, behavioral science, toxicology
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modeling of protein dynamics We are seeking a highly motivated PhD student to join a DDLS-funded project at the interface of structural proteomics, protein biophysics, and machine learning. The position is