**Job Description**
This research-focused position involves the study and development of methodologies for analyzing complex multidimensional networks, which are represented using tensors. The primary goal is to address the challenge of network completion, particularly for sparse and multidimensional data, by applying and developing tensor completion methods, incorporating auxiliary information, and exploring sparse optimization techniques and randomized algorithms. The role also emphasizes the exploration of parallel computing opportunities for computational efficiency.
**Skills & Abilities**
• Numerical Linear Algebra
• Tensor Analysis
• Network Science
• Algorithm Design and Implementation
• Optimization Techniques
• Tensor Decompositions
• Sparse Optimization
• Randomized Algorithms
• Parallel Computing
• Interdisciplinary Collaboration
• Research Publication and Dissemination
• Mentorship
**Qualifications**
Required Degree(s) in:
• Applied Mathematics
• Numerical Linear Algebra
**Experience**
Other:
• Prior experience on the subject (tensor completion, multidimensional networks) is highly desired.
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