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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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partner in an exciting 5-year collaborative program. We are currently seeking applicants for three PhD projects as listed below. The successful candidates will be based in either NIBRT or UCD and will also
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, preferably Reinforcement Learning (e.g., Q-learning, Deep Q-Networks) or other control algorithms. Proficiency in Python, MATLAB, or similar for data analysis, modeling, or AI implementation. Strong written
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water
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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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that can be used for training machine learning and deep learning models. You will work in tight collaboration with other researchers in Nijmegen, Delft and at the Hubrecht Institute (van Oudenaarden group
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: Please visit “Why Carnegie Mellon ” to learn more about becoming part of an institution inspiring innovations that change the world. Click here to view a listing of employee benefits Carnegie Mellon
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centrifugal, digital, capillary, pressure, or microvalve-based microfluidics. Experience in deep-learning and artificial intelligence in the field of microfluidics to support applications such as high
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related discipline is required at the start of the fellowship. Required materials: Letter of interest (cover letter) CV Research statement List of three references Exemplar publication or pre-pub
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03.06.2021, Wissenschaftliches Personal The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and