13 algorithm-development-"https:"-"Technical-University-of-Cluj-Napoca"-"RAEGE-Az" positions at AALTO UNIVERSITY
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allows the successful applicant to choose a flexible balance between advanced machine learning method development and exciting applications on molecular data, including biomedicine and drug discovery
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research group of Professor Juho Rousu. The position allows the successful applicant to choose a flexible balance between advanced machine learning method development and exciting applications on molecular
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algorithms and data analytics tools. You will also discuss tool development with AI and machine learning experts at Aalto University and share your results with experimental collaborators. The position is part
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on biomedical engineering, biophysics, and brain research. We combine experimental and computational methods and develop algorithms, as well as new technologies to tackle major problems in human well-being and
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. We are experts in systems science; we develop integrated solutions from care of the elderly to space robotics. We are now looking for a Postdoctoral Researcher in quantum computing and algorithms
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information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated solutions from care
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diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community. The Electric Drives Group is seeking two postdoctoral researchers to develop
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to advanced control design and system optimization. Our specialty is developing embedded control, estimation, and identification algorithms that directly interface with physical hardware. We work closely with
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transport for inverse problems One of the central topics of the research projects is the further development of theory and methods for the concept of optimal transport for inverse problems. Optimal transport
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collaborators. Your work will develop algorithms, inference methods, and frameworks to adapt models from training data to test environments, which is necessary to resolve distribution shifts, hidden confounders