Postdoctoral Associate

Postdoctoral Research Associate The postdoctoral research associate is responsible for developing novel computational methodology for high-throughput sequence genomics tasks, as well as for building efficient and robust implementations of these methods. The primary focus is on the development of methods for single-cell transcriptomic (RNA-seq) and ATAC-seq data, specifically in the single-cell context, though projects related to long-read RNA-sequencing and to foundational algorithmic and data structural questions is also within scope. The postdoctoral research associate leads their own project, as well as to collaborating with other members of the lab. The Patro lab has a strong commitment to open science, and to developing efficient, robust, usable and free and open-source software. Duties include: Designing and developing novel computational methodologies for the processing and analysis of high-throughput sequencing data Writing robust and efficient implementations of novel computational methods in a "high-performance" programming language (Rust, C, C++, Zig) and integrating these new tools with existing software developed by the lab Preparing, writing, and editing scientific manuscripts Collaborating with and co-mentoring PhD students Assisting in the writing and preparation of funding proposals (NSF, NIH, and other relevant foundations or opportunities) Minimum qualifications: Education: PhD in Computer Science or a closely-related field Experience: Experience in performing independent research in algorithmic genomics, and communicating scientific results via scientific publications and presentations. Experience with handling and analyzing large genomics datasets, as well as experience in developing software in compiled, statically-typed, garbage-collection-free languages (e.g. Rust, C, C++, Zig). Other: Strong written and oral communication skills. Strong interpersonal skills. Physical Demands: Must be capable of maintaining a stationary position for extended periods. Job requires close-up vision for computer work. Preferences: Experience developing advanced computational methodology for genomics data processing Experience with and knowledge of compact and succinct data structures Experience with and knowledge of methods for high-dimensional statistical inference (e.g. generative graphical models, maximum likelihood inference, Bayesian inference, etc.) Knowledge of or willingness to learn the Rust programming language Experience with data analysis and visualization Familiarity with common genomics tools, formats and programs Experience with standard *-nix command-line tools and idioms Familiarity with reproducible scientific workflow systems, such as Nextflow or Snakemake Licenses/ Certifications: N/A Umcp