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and the efficiency of triple transfections of HEK cells and apply the learnings to the production of adeno-associated virus (AAV) gene therapies. 2] To develop a process model suitable for application
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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, Large Language Models, Human-Computer Interaction, Virtual reality. The selected candidate will work on the design and implementation of a human-computer interface to support education using an AI-based
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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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Europe. In the Monitoring & AI department, you will be involved in the development and implementation of AI and machine learning (ML) tools for monitoring and operation of CO2 storage sites. Key
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years
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validating deep learning models for the prediction of disease progression from ophthalmic data. Skills include working with image or computer vision-based toolkits, development of multimodal, multidata type
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-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated evidence of excellent programmin g, collaboration