Artificial Intelligence Researcher (PhD)

Company Description PaperScore is a pioneering decentralized academic journal and publication platform that replaces traditional editorial boards with a collective intelligence algorithm to manage peer review. Implemented as backend code, this algorithm selects reviewers based on manuscript keywords, citations, and referrals, enabling an unbiased and efficient review process across all disciplines, with a strong focus on interdisciplinary and multidisciplinary research. By decentralizing the review system, PaperScore aims to break the entrenched network effects among authors, institutions, and journals that can lead to self-fulfilling reputational cycles. The platform provides a home for high-quality studies that may be overlooked due to editorial biases such as significance bias and discipline bias. Leveraging small-world referral chains and purposeful randomization, PaperScore seeks to connect manuscripts with suitable reviewers in just a few steps, supported by appropriate incentives.Role Description The Artificial Intelligence Researcher (PhD) role at PaperScore is a full-time, on-site position based in the Austin, Texas Metropolitan Area. The researcher will design, implement, and evaluate algorithms that power PaperScore’s decentralized review and recommendation systems, including models for collective intelligence, pattern recognition, and reviewer selection. Day-to-day responsibilities include conducting original research, developing prototypes, running experiments, analyzing large-scale data from manuscripts and reviews, and publishing findings in academic venues where appropriate. The role involves close collaboration with engineering and product teams to translate research insights into robust, production-ready systems, as well as continuous improvement of statistical and machine learning models governing bias reduction and fairness. The researcher is expected to stay current with advances in AI, data science, and scholarly publishing technologies, and to contribute to strategic decisions about the platform’s algorithmic roadmap.Qualifications Strong foundation in Computer Science, including algorithms, data structures, and software engineering principles.Expertise in Pattern Recognition and Data Science for developing, training, and evaluating machine learning and AI models.Demonstrated experience in Research and Statistics, including experimental design, hypothesis testing, and quantitative analysis.PhD in Computer Science, Data Science, Statistics, Artificial Intelligence, or a closely related field.Proficiency in programming languages commonly used in AI research (e.g., Python, C++, or similar) and relevant libraries (e.g., PyTorch, TensorFlow, scikit-learn).Experience working with large, complex datasets and building scalable data processing pipelines.Background or interest in academic publishing, peer review systems, or decentralized platforms is highly beneficial.Ability to communicate complex technical concepts clearly to interdisciplinary stakeholders and collaborate in a diverse team environment.