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analysis using deep learning algorithms Detecting anomalies and dependence structures in high dimensional, high frequency financial data During the project, the PhD students will closely collaborate with
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. Candidates must demonstrate that they have contributions and publications in the field of statistics and stochastic modeling in finance, risk management, sustainability, demonstrating expertise and independent
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sustainability component The description here focuses on the specific position " Predicting financial trends using text mining and NLP (IRP 13)" (mainly included in objective 1 – WP1), outlining the project's key
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registration The open position (job) is within a project financed within Romania’s National Recovery and Resilience Plan, Pillar III.Smart, sustainable and inclusive growth, including economic cohesion, jobs
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. Candidates must demonstrate that they have contributions and publications in the field of statistics and stochastic modeling in finance, risk management, sustainability, demonstrating expertise and independent
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. Applicants with a solid practical experience in statistics and stochastic modeling with applicability in microeconomics, macroeconomie and sustainability are particularly welcome, Experience in previous
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, 2026. Candidates must demonstrate that they have contributions and publications in the field of statistics and stochastic modeling in finance, risk management, sustainability, demonstrating expertise and
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modeling with applicability in microeconomics, macroeconomie and sustainability are particularly welcome, Experience in previous similar projects will be considered a major advantage. You should have a
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PhD level in the field of economics, (business) mathematics, (business) informatics, statistics or similar with above-average success. As a member of our team, you will deal with challenging questions
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PhD level in the field of economics, (business) mathematics, (business) informatics, statistics or similar with above-average success. Creativity, willingness to learn, scientific-oriented thinking as