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Details Title Postdoctoral Fellow in Riemannian Optimization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description A postdoctoral position is
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learning; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic
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guarantees. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year
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quantitative field (by the time of appointment). Strong background in statistical or machine learning methodology, optimization, or high-dimensional data analysis. Proficiency in R or Python; experience with
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, surgical procedures, and electrophysiology. The candidate is expected to operate independently within defined protocols and contribute to troubleshooting and optimization of experimental systems. Basic
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, assistance with the design, assembly, testing, and optimization of laboratory instrumentation, and collection and analysis of experimental data. The individual will also provide support for automation and
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HPC concepts, including parallel computing, distributed systems, and optimization. Analytical skills, problem-solving abilities, and a growth mindset. Additional Qualifications Applicants should be
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. · Lead the implementation and optimization of high-throughput functional platforms, including: o Single-unit extracellular recordings (e.g., high-density MEA systems, neural probes) o