The applicant for the aforementioned position shall meet the requirements of Art. 113 and Art. 116 of the Law on Higher Education and Science dated 20 July 2018 (Journal of Laws of 2024, item 1571 with amendments) and the Statute of the University of Lodz.

The applicant must meet the condition of not having a criminal record with respect to the offences specified in Chapters XIX and XXV of the Penal Code in Art. 189a and 207 of the Penal Code, and in the Act of July 29, 2005, on Counteracting Drug Addiction (Journal of Laws of 2023, item 1939), as well as the corresponding offences specified in foreign laws.

Required knowledge:

·   theory and practice of corporate finance,

·  methods for evaluating investment efficiency,

·  financial analysis and financial modeling tools,

·  issues related to sustainable finance and ESG (including EU regulations),

·  issues related to financial markets and international investments,

·  issues related to AI in the corporate context,

A candidate applying for the position should have experience in:

·    conducting scientific research both independently and as part of a research team, as well as publishing in academic journals,

·    applying for research grants and securing research projects,

·    implementing scientific projects at both national and international levels,

·    cooperating with foreign academic institutions to establish contacts and conduct joint research,

·    using and analyzing various databases (e.g. LSEG EIKON, Bloomberg),

·    carrying out projects in a business environment / in cooperation with the business sector,

·     implementing artificial intelligence–based solutions in finance,

The candidate applying for the position should be able to demonstrate at least one published scientific article in an academic journal, and a conference paper presented at an international conference.

The candidate should possess competencies and skills relevant to teaching and research activities, including:

·   knowledge of modern analytical tools and digital technologies used in finance,

·  practical skills in using artificial intelligence (AI) tools, in particular in:

• data analysis,
• automation of analytical processes,
• conducting scientific research,

·  experience in project-based work and project management,

·  the ability to teach courses in Polish and English (mandatory requirement),

· competencies in preparing scientific publications and applying for research grants,

· possession of professional certificates and specialist training.

The candidate should plan further academic and teaching development in the area of corporate finance and sustainable finance, with particular emphasis on:

·  conducting research on the integration of ESG factors in corporate financial management,

·  developing methods for the use of artificial intelligence in financial analysis and decision-making processes,

·  publishing research results in reputable academic journals,

·  active participation in national and international research projects,

· development of teaching competencies, including the creation of modern curricula,

·  building international scientific cooperation.

The applicants should apply by sending the following documents:

-         a personal questionnaire and statements available on the University of  Lodz website https://www.uni.lodz.pl/kariera

-        a copy of the PhD diploma and any other diplomas the applicant has obtained,

-        CV, including a list of publications, main conference presentations and research projects, evidence of language skills (e.g. certificate)

We reserve the right to conduct interviews with candidates.

In case of failure to submit the statements (of no criminal record and for verification purposes) by the recruitment deadline, we reserve the right to leave the application without consideration.

Winning the competition does not guarantee employment at the University of Lodz.

economics and finance

International Finance and Investments, Corporate Finance, Data Analysis, International Business, Financial Analysis, Financial Modeling