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Field
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-time inference within rendering pipelines Minimum Qualifications: • Ph.D. in Computer Science, Visual Computing, Interactive Media, or related field • Strong C++ and/or C# programming expertise
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algorithms for analysis of large image datasets. Experience with parallel computing, high-performance computing, distributed computing, and/or cloud-based computational workflows. Experience with large-scale
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, contrastive learning, or representation learning approaches Experience with high-performance computing, cloud platforms, or distributed training Prior experience working in a collaborative biomedical
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federated learning SOTA algorithms. Knowledge of distributed optimization and consensus algorithms. Knowledge of large models and hyper-parameter optimization. Knowledge of high-performance computing and its
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: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling, representational analysis, or
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, high-performance computing, distributed computing, or large-scale workflow management. Experience developing reconstruction, simulation, or detector calibration software. Familiarity with modern machine
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excellent infrastructure for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing
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infrastructure for deep learning, speech, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure
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career taken into consideration. Additionally, your resume must comprise: A PhD or equivalent Research experience in Software Engineering, Distributed Software Systems, Edge and Cloud Computing A
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). We are looking for a person with: A PhD in computer science or a related field, with a software systems profile: distributed systems, cloud or edge computing, simulation, digital twins, model-based