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following criteria: PhD in Computer Engineering, Computer Science, Electrical Engineering, or a closely related field Demonstrated research excellence, evidenced by peer-reviewed publications Expertise in
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https://engineering.purdue.edu/PMRI). The population of officers at Purdue currently exceeds 100 students pursuing PhDs and MS degrees. We intent to grow this number to build a population of unique
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familiar with data science and machine/deep learning toolkits. Experience with model deployment and the usage of MLOps tools (Dockerization, CI/CD pipelines, edge infrastructure, etc.) is a plus. As a PhD
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SAF combustion. Recent advances have demonstrated that machine learning techniques, particularly neural networks, can significantly accelerate chemical kinetics computations. Nevertheless, most of
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be also encouraged to explore other research opportunities or collaborations within the group. Tools and techniques to be developed include but are not limited to MR pulse sequences, machine learning
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will be integrated with statistical and machine-learning methods to classify polarity states and identify quantitative signatures predictive of metastatic behavior. The project will deliver transferable
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 23 hours ago
for data-intensive digital systems research. The Department of Machine Learning for Infection and Disease is looking for a Postdoc (f/m/d) on Generative AI for de Novo Protein Design. Your tasks # Design of
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accordance with FAIR principles, combined with skills in statistical analysis, machine learning and/or data science. Experience with programming languages such as R, Python, or similar will be considered
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skills and experience: Essential criteria PhD or equivalent (or thesis submitted*) in at least one of the following subjects: Computer Science, Machine Learning, Biomedical Engineering, Medical Imaging
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow