Alexandros Iosifidis

Tampere University is seeking a Doctoral Researcher to join a project focused on developing advanced machine learning and deep learning methods for financial data analysis. The research topics include deep learning for time series, high-frequency market data, generative models, and trustworthy AI. This position is ideal for candidates with a strong background in computer science, engineering, or data science, and a keen interest in research related to financial markets. The successful applicant will work under the supervision of Professor Alexandros Iosifidis at Tampere University, Finland. The research will involve designing and implementing novel algorithms for analyzing complex financial datasets, with applications in market prediction, risk assessment, and trustworthy AI systems. The position offers an excellent opportunity to contribute to cutting-edge research at the intersection of machine learning and finance. Applicants should hold a Master’s degree in a relevant field and possess solid machine learning skills. Experience with deep learning, time series analysis, and financial data is highly desirable. The application deadline is 27 February 2026. For more information and to apply, visit the Tampere University recruitment portal.

Tampere University invites applications for a Doctoral Researcher (PhD student) position focused on Deep Learning for Financial Markets. This opportunity is part of the Computational Intelligence group led by Professor Alexandros Iosifidis, in collaboration with Professor Juho Kanniainen’s Financial Computing and Data Analytics group at the Unit of Computing Sciences. The position is based at the Hervanta Campus in Tampere, Finland, and offers a vibrant, multidisciplinary research environment with strong ties to the ELLIS Institute Finland, a world-class hub for AI and machine learning research. The research project centers on designing, implementing, and advancing new machine and deep learning methods for financial market applications. Key topics include high-frequency time-series forecasting, generative models for financial data synthesis and prediction, trustworthy financial data predictions, and world models for financial markets. The project combines real-world financial data analysis with hands-on development of machine learning models, offering flexibility for the researcher to shape the research direction according to their interests. Publishing high-quality scientific papers is a core expectation, and the role may include minor teaching or faculty tasks (5-10%). Applicants must hold a completed master’s degree in computer science, computer engineering, or a related field before the employment start date. Essential qualifications include strong knowledge of machine learning theory and deep learning methods, proficiency in Python programming and scientific libraries (PyTorch or TensorFlow), and the ability to conduct systematic scientific research. Experience with generative AI methodologies and financial data analysis, especially Limit Order Book data, is advantageous. Excellent English communication skills and the ability to collaborate in multidisciplinary teams are required. Candidates must be able to write research papers for peer-reviewed journals or conferences. If the applicant’s prior degree is not from Finland, an official English language certificate is required for admission to the doctoral programme. The position is full-time and fixed-term for four years, with a planned starting date of April 1st, 2026 or as mutually agreed. The starting salary is approximately 2700 EUR/month, increasing with experience and progress in doctoral studies, and is determined by the Finnish University Salary System (level 2-4). Benefits include occupational health care, flexible work schedule, access to versatile research infrastructure and modern teaching facilities, a safe campus environment, and a personal fund for sports and cultural activities. Tampere University offers a dynamic and international research community, with a strong commitment to scientific excellence and high-quality doctoral education. The Data Science Research Centre brings together experts in fundamental and applied machine learning, fostering interdisciplinary collaboration and innovation. The city of Tampere is a major academic hub in the Nordic region, known for its vibrant entrepreneurial community, rich cultural scene, and dynamic living environment. To apply, submit your application via the Tampere University online recruitment system by February 27th, 2026. Applications must be in English and include a CV, application letter (max. 3 pages), degree certificates and transcripts, and other relevant supporting documents. For further information, contact Professor Alexandros Iosifidis at [email protected].

Tampere University invites applications for two Doctoral Researcher positions in Deep Learning for Machine and Robot Perception, funded by the Horizon Europe project OPERA. The successful candidates will join the Computational Intelligence group led by Professor Alexandros Iosifidis and collaborate with the Signal Analysis and Machine Intelligence group led by Professor Moncef Gabbouj. The positions are based at the Hervanta Campus in Finland, within the Computing Sciences Unit of the Faculty of Information Technology and Communication Sciences. The research will focus on developing novel deep learning methodologies for machine and robot perception, with topics including self-supervised and complementary learning strategies, social signal analysis, collaborative learning, robustness and adaptation, adaptive and robust human-robot and robot-environment interaction, multimodal human-centric understanding, and active sensor fusion. The positions offer opportunities to disseminate research results in international conferences and journals, interact with project partners, and integrate developed methodologies into the OPERA project toolbox. Teaching duties may comprise 5-10% of the workload, aligned with the researchers' expertise. Applicants must hold a completed master's degree in computer science, computer engineering, or a related field, with strong knowledge of machine learning theory and deep learning approaches. Proficiency in Python and scientific libraries such as PyTorch or TensorFlow is essential, along with the ability to conduct systematic scientific research, critical thinking, problem solving, and excellent communication skills in English. Candidates must be able to write research papers for peer-reviewed journals or conferences. Admission to the doctoral programme of Computing and Electrical Engineering at Tampere University is required, including an official English language certificate if the university degree was not completed in Finland. The positions are full-time and fixed-term, starting from October 1st, 2026, until September 30th, 2030, with possible adjustment if the start date is later. The typical starting salary is approximately 2700 EUR/month, increasing with experience and progress in doctoral studies. Staff benefits include occupational health care, flexible work schedule, access to versatile research infrastructure and modern teaching facilities, a safe and inviting campus, and a personal fund for sports and cultural activities. Tampere is a vibrant academic hub in the Nordic region, offering a dynamic living environment and a rich cultural scene. To apply, submit your application through the Tampere University online recruitment system by May 4th, 2026. Applications must be written in English and include a CV, application letter, degree certificates, transcripts, and other relevant documents. For further information, contact Professor Alexandros Iosifidis ([email protected]) or Professor Moncef Gabbouj ([email protected]).

Tampere University is advertising a full-time Doctoral Researcher position in Deep Learning for High-Frequency Financial Data Modeling within the Unit of Computing Sciences in Finland. The selected researcher will join the Computational Intelligence group led by Professor Alexandros Iosifidis and collaborate closely with the Financial Data Science group led by Professor Juho Kanniainen . The project focuses on designing, implementing, and proposing new machine learning and deep learning methods for financial markets . Research topics include generative models for financial data synthesis and forecasting, trustworthy financial data predictions, world models for financial markets, system-II predictive models, and multi-model multi-asset routing mechanisms for financial prediction. The role combines real-world financial data analysis with hands-on model development and the expectation to publish high-level scientific work. This is a four-year fixed-term doctoral position located at Hervanta Campus, Tampere, Finland . The position is full-time and may include some teaching or supporting faculty tasks (about 5–10%). The planned starting date is 1 December 2026 or as mutually agreed. Eligibility highlights include a completed master’s degree in computer science, computer engineering, or a closely related field; strong knowledge of machine learning and deep learning; ability to perform systematic scientific research; strong critical thinking and problem-solving skills; and excellent English communication abilities. Experience with generative AI is valued, and familiarity with financial data analysis, especially limit order book data , is considered an advantage. Applicants must also qualify for doctoral study rights in the Doctoral Programme of Computing and Electrical Engineering at Tampere University, and non-Finland degrees may require an official English language certificate. Funding is provided through the salaried doctoral researcher appointment, with a typical starting salary of approximately EUR 2,770–2,900 per month under the Finnish University Salary System. Benefits mentioned include occupational health care, flexible working arrangements, research infrastructure, modern facilities, a campus environment, and sports/cultural support. Applications must be submitted through the university’s online recruitment system by 2026-10-05 . Required attachments include a CV, a free-form application letter describing fit and research interests, degree certificates and transcripts, and optional supporting materials such as a thesis, up to two relevant publications, and a list of referees.