We help acemedia (universities and R&D institutions) find top class researchers in deeptech - with a particular focus on Artifical Intellegence and Computing technologies. We rank candidates for academia thereby producing an effective first filter on research position applications.
Recruiting top researchers in deep tech disciplines is a critical challenge for academic institutions worldwide, particularly as global demand for expertise in artificial intelligence (AI), machine learning, and advanced computing technologies continues to outpace supply. Scientometrics.ai addresses this challenge by offering a structured, data-driven platform that enables universities and research institutions to identify, evaluate, and shortlist high-performing candidates based on research output and impact metrics.
In academic hiring, traditional processes - such as manual CV screening and peer recommendation - are increasingly inadequate for processing large volumes of applications, especially for globally advertised roles. Scientometrics.ai enhances this pipeline by providing automated, objective, and transparent rankings of researchers based on bibliometric indicators such as the h-index, citation counts, field-weighted impact, and publication venue quality. These metrics, when properly normalized and field-adjusted, are widely used in higher education for evaluating academic performance and tenure-track progression.
Recent studies (e.g., Ioannidis et al., PLoS Biology, 2020) have demonstrated the value of composite citation indicators for comparing scientific output across disciplines. Scientometrics.ai integrates such principles into its ranking models, offering a robust first filter for identifying leading candidates in AI and computing - fields characterized by high publication volumes and rapid innovation cycles.
HR departments in academia
For HR departments in academia, this product offers both time savings and quality control. Hiring committees can begin with a shortlist of top-ranked researchers in a specific subdomain (e.g., reinforcement learning, computer vision, or distributed systems), based on real-time performance data. Furthermore, institutions offering PhD studentships or postdoctoral fellowships can use this dataset to proactively identify emerging talent - including junior researchers or doctoral students with strong publication profiles in tier-1 conferences (such as NeurIPS, ICML, or CVPR).
Other Research institutions
Research institutions aiming to build strategic capacity in deep tech domains - such as national AI research centers or computing innovation hubs - can also leverage these rankings to scout potential collaborators, evaluate internal benchmarks, or assess external applicants to faculty and staff positions. The system’s emphasis on normalized metrics ensures fairness across diverse applicant pools, including those from non-English-speaking countries or newer research institutions.
scientometrics.ai provides an essential recruitment intelligence layer tailored for academic deep tech hiring. It augments institutional decision-making with quantitative rigor, helping ensure that the next generation of AI and computing researchers are selected not just through networks or credentials, but through demonstrated scientific contribution.