Experienced researcher for the project CauseFinder

The Bucharest University of Economic Studies has opened 1 Experienced Researcher positions.

Part-time 40 hours/month distributed unequally, gross hourly salary 241.50 lei, fixed period of 12 months with evaluation and possibility of extension until June 30, 2026.

The project is entitled “CauseFinder: Causality in the Era of Big Data and AI and its applications to innovation management”, having Prof. Dr. Dumitru Roman as principal investigator.

The ideal candidate profile consists of advanced knowledge in statistical and econometric modeling, scientific-oriented thinking, communication and team skills, with a PhD in the field of economics, (business) mathematics, (business) informatics, statistics or similar, and a minimum 8 years of scientific research experience.

As a member of the team, you will deal with challenging questions related to artificial intelligence, particularly knowledge graph theory, causal modeling, probabilistic reasoning and anomaly detection. Within the framework of your assignment, you will have the opportunity to present your results at international conferences. Our team offers flexible working hours and intensive cooperation in a committed team.

The application deadline is March 18, 2024. If you have any questions, please contact Prof. Giani Gradinaru ([email protected] ). You can find more details below, as well as a short presentation of the project.

The next wave of Big Data and Artificial Intelligence (AI) will be centered on extracting deeper structure from observed systems. Built on the rapid development of existing Data and AI technologies, topics such as Multilevel Evolving Knowledge Graphs, Causal Reasoning, Explanation Based Modeling will result in much more efficient solutions than possible today.

The project aims at groundbreaking results in theory, methodology and algorithms, especially in the fields of scalable multiresolution causality modeling, scalable probabilistic reasoning, evolving knowledge graph construction from diverse data sources and new insights into innovation ecosystem lifecycle management.

Methodologically, the starting points are the state-of-the-art Big Data and AI approaches and tools. The project targets scenarios where we monitor multiple complementary global near real-time data streams, interconnect them into an evolving probabilistic causal knowledge graph, prepare an operational algorithmic platform, answer and explain complex questions about the known and possibly unknown phenomena, address ethical and inequality protective issues, and apply the developed methods to the domain of the innovation ecosystem management. The use case proposed to validate the expected results is in the field of modeling of innovation lifecycle. Data streams from a variety of sources will be used as a baseline, covering different aspects of the global innovation dynamics in near real-time. Specifically, we will focus on the field of AI across many stages of its ecosystem. Sample questions we aim to answer in this context include: causal impact of research policies on the innovation ecosystem lifecycle, answering ‘what-if’ questions, predicting trends and disruptions along the innovation ecosystem, predicting the impact of science on the job market.

• Solid knowledge of statistics and probability, graph theory, causal modeling, probabilistic reasoning in artificial intelligence, anomaly detection in data mining;
• Experience in data analysis, statistical and econometric modeling;
• Good skills in writing scientific papers;
• Knowledge of the English language at an advanced level;
• Good organizational, communication and collaboration skills with international and multidisciplinary teams.

• Collaboration with the project team to carry out research activities, including reports, studies and scientific articles;
• Supporting and leading the project team in creation of algorithms, code snippets and development of statistical methods to achieve the desired results;
• Assuring the partial and final results’ quality and correctness;
• Collaboration with the project team to disseminate the partial and final results;
• Supporting the project team in achieving the assumed objectives.

Please see https://fondurieuropene.ase.ro/anunturi/