kabfi ∂does-not-exist.fbv kit edu

We are a research training group driven by the opportunities of the data age. As an interdisciplinary team, we embrace the complexity of real-world problems—messy, incomplete, and often ill-defined—and train our Ph.D. researchers to do the same. Our program equips high-potential candidates with the skills to address challenges at the intersection of Artificial Intelligence, Data Science, and Business. Through specialized coursework, close mentorship, and access to unique high-frequency datasets, we prepare the next generation of scholars to turn data into insight—and insight into impact.

Our approach is grounded in data-driven decision-making. We combine machine learning, modern econometrics, and economic thinking to reduce uncertainty and support better decisions in complex systems. While our methods apply across a broad range of industries, our research is particularly rooted in financial and option markets—where data volume, modeling depth, and business relevance converge. Ph.D. candidates gain hands-on research experience, engage in publication-focused projects, and develop the technical and analytical skillset to thrive in both academia and industry.

The KABFI Ph.D. community is proudly international, with researchers from Vietnam, Iran, China, the USA, Germany, and beyond. Many of our Ph.D. students combine their research with part-time roles at KIT, international institutions, or within industry. Their diverse academic backgrounds—from finance and economics to computer science and engineering—reflect the interdisciplinary and applied nature of the program; their career paths, its practical relevance and global outlook.

Our focus on intellectual rigor and research-based learning resonates with students at various stages of their careers. One graduate recently joined Barclays in London as a quantitative researcher in asset management after impressing practitioners with his ability to connect mathematical detail with economic intuition—an ability honed through our data science and finance training. Another former student, after accepting an offer at Morgan Stanley in New York, wrote:
“I would love to pursue a PhD, but for now, I need to focus on learning my new job… I am also not ruling out the possibility of returning to you to discuss a PhD at some point in the future.”
We welcome such trajectories. Whether directly after a Bachelor's / Master's degree or following industry experience, we support students in building a path toward academic inquiry and long-term research development.

Join a community of bright and driven Ph.D. researchers at KABFI (pronounced “cab-fee”)—KIT’s AI, Business, and Financial Markets Insights PhD Research Training Group—dedicated to advancing the synergy of artificial intelligence, financial markets, and data-driven business applications. KABFI welcomes a select group of individuals with a passion for rigorous research, a strong foundation in data and markets, and a vision to shape the future of AI-enhanced decision-making in business and finance.

Collaboration is central to the KABFI experience. We are currently expanding our academic partnerships to include computer science departments at other German universities that are exploring the application of machine learning and data science to financial problems. We also maintain active collaborations with researchers in Germany and abroad, ensuring our Ph.D. students work on cutting-edge research questions with access to a broad, international academic network.

For those interested in deepening their mathematical and computational focus, collaboration with other KIT programs—such as the Graduate School Computational and Data Science (KCDS)—offers additional opportunities for interdisciplinary Ph.D. research.

• Academic Aim: At KABFI, we foster a collaborative research culture in which incoming Ph.D. students are mentored by both faculty members and more senior Ph.D. researchers. Our goal is to support joint, high-impact publications in leading international journals across financial economics, machine learning, artificial intelligence, and statistical modeling. Previous KABFI research has appeared in prestigious outlets such as The Review of Financial Studies and The Review of Derivatives Research.

• Financials:  KABFI offers a tuition-free Ph.D. education. Ph.D. researchers are responsible for their own living expenses. To support this, we assist in securing scholarships and part-time positions, both within KIT and through industry partners. In recent years, KABFI students have successfully obtained competitive scholarships, programming roles, and research assistantships.

   

• Admission:  We welcome applications from motivated and talented candidates year-round. In addition to traditional Master’s-track entry, we are currently exploring a new pathway in collaboration with computer science departments at other German universities. In some of these programs, highly motivated Bachelor’s students with a strong background in computer science may directly transition into the Ph.D., provided they are formally accepted and complete an appropriate coursework package. This coursework could combine KABFI modules in finance and data science with graduate-level courses from the collaborating computer science department. Candidates interested in this option are encouraged to reach out directly to discuss their fit and the academic framework.

  We also invite exceptional Bachelor's degree holders—particularly those ranked among the top 5% of their cohort—to apply for the fast-track Ph.D. entry at KIT. In general, applicants with a Master’s degree in a relevant discipline and strong academic performance (typically ranging from good (B) to very good (A)) are encouraged to apply. Please submit your CV, a motivation letter, and academic transcripts to: kabfi∂fbv kit edu

• Coursework: KABFI students complete core training modules and actively engage in weekly research discussions:

• Financial Data Science (9 ECTS): An advanced Bachelor-level exploration into the art and science of working with financial data. https://risk.fbv.kit.edu/c-ram/2297.php
• Advanced Machine Learning and Data Science (9 ECTS): A hands-on, coding-oriented research project bridging AI and finance. https://risk.fbv.kit.edu/c-ram/2307.php
• Deep Financial Economics: A weekly seminar series where students critically assess the impact and quality of recent publications in AI, financial economics, and data science. https://risk.fbv.kit.edu/c-ram/2307.php

            Students without prior academic training in finance may attend selected MBA-level focus weeks. These modules cover essential foundations in:

• Global Financial Markets
• Financial Economics for Data Scientists
• Machine Learning for Data-Driven Decision Making
• Engineering Aspects of Financial Markets
• Alternative Data and Machine Learning for Business Applications

• Relocation & Flexibility: While we strongly encourage Ph.D. researchers to join us on campus in Karlsruhe to benefit fully from the academic environment, select hybrid arrangements have proven successful in the past. We remain open to flexible models for outstanding candidates, particularly where professional or personal circumstances require it. In cases of joint supervision or collaboration—such as with partner computer science departments at other universities—students may also choose to locate in a region accessible to both institutions.

The KABFI Ph.D. program offers several Fast Track pathways for exceptional students who wish to accelerate their academic journey and research career.

1. Direct Entry from Bachelor’s Degree
Outstanding Bachelor students—those who can demonstrate they are in the top 5% (95th percentile) of their cohort—have the opportunity to enter the KABFI Ph.D. program directly, without completing a Master’s degree. This route is designed for highly motivated candidates with strong quantitative skills and academic maturity. In such cases, we anticipate that the Ph.D. thesis can be submitted shortly after their peers complete their Master’s studies. All formal requirements of the applicable Promotionsordnung apply.

2. Integrated Fast Track for KIT Students
KIT Bachelor and Master students who aim for an early start in research can follow an integrated Fast Track. These students complete the KABFI core Ph.D. coursework (see “Coursework” section) during their MSc studies at KIT. Additionally, if both the BSc and MSc thesis are written in collaboration with KABFI researchers (implying serious KABFI research efforts during their BSc and MSc studies), it is typically possible to submit the Ph.D. thesis within 18 months of MSc graduation.

3. Emerging Pathway via Partner Institutions
In collaboration with computer science departments at other German universities, we are exploring a third fast-track route. Under certain Ph.D. regulations, Bachelor students may enter a Ph.D. program directly if selected graduate-level coursework is completed in parallel. In such cases, KABFI doctoral training can be combined with coursework at the partner institution—e.g., machine learning and computational methods from computer science, and financial economics and data science from KABFI. Interested candidates with a strong Bachelor-level background in computer science or quantitative disciplines are encouraged to contact us to discuss this evolving option in more detail.

Research Support:
At KABFI, Ph.D. researchers benefit from access to state-of-the-art high-performance computing clusters and a broad range of financial databases covering both U.S. and European markets. A particular strength of our infrastructure is our proprietary tick-by-tick option analytics database and the complete order book data for all assets traded on Europe’s largest exchange. This unique dataset is generously provided by Deutsche Börse Group to support cutting-edge research and education in financial data science—a testament to the commitment of leading financial institutions to advancing quantitative research and talent development at KABFI. To ensure a smooth start, more senior Ph.D. students offer hands-on guidance and coaching, an integral part of our mentoring culture designed to help you unlock your full research potential.

Research Topics:
Our research spans a wide range of interdisciplinary fields, with applications in financial economics and engineering, computational risk and asset management, and machine learning for capital market analytics. Topics include the development of supervised learning techniques to address complex business challenges, as well as the design of autonomous systems using financial reinforcement learning. Ph.D. candidates are supported by experienced researchers who meet them at their current level and help them grow into independent scholars equipped to make meaningful contributions in both academia and industry.

Collaborations with computer science departments at other universities further expand the range of research topics and supervision available—particularly in machine learning, data science, and algorithmic finance. These partnerships open opportunities for co-supervised projects, hybrid coursework models, and exposure to complementary research environments in computer science and financial economics.

KABFI alumni have received multiple job offers even before completing their dissertations—often choosing between roles in international financial hubs and high-impact positions closer to home. Our graduates have gone on to pursue careers in quantitative finance, asset and risk management, consulting, and AI-driven entrepreneurship.

Some students begin the KABFI journey with a clear academic focus, only to be drawn into competitive industry roles by the strength of their training. One student, originally accepted into the KABFI Ph.D. program, relocated to New York for personal reasons and applied for jobs to bridge the transition. His strong performance in KABFI’s preparatory courses—covering topics such as factor models, the stochastic discount factor, and machine learning—attracted immediate attention from leading financial firms. He ultimately accepted an offer from Morgan Stanley, writing:
“Your classes truly prepared me for the interviews, and our project gave me a lot to discuss. Topics ranging from factor models to stochastic discount factor and machine learning were part of the conversations, and the way you taught us about these subjects gave me the confidence to speak about them.”

Another graduate, after completing the KABFI program, joined Barclays International in London as a quantitative researcher in asset management. Following several rounds of interviews, he shared:
“The interviews were like your lecture notes. I was asked to go to the whiteboard and prove data science concepts relevant for financial research. What impressed the interviewers most was not just the technical depth, but that I could connect the math and concepts with economic thinking and intuition. That made the difference.”

While not all paths are linear, the analytical depth, technical rigor, and mentoring culture at KABFI equip our students to thrive in both academia and industry—and to navigate between the two as opportunities arise.

We gratefully acknowledge the generous support of our corporate partners, whose contributions—often made via the Forschungsgesellschaft Geld-Banken-Bausparkassen-Versicherungen am KIT e.V.—help make doctoral research scholarships at KABFI possible. These funds enable us to support outstanding Ph.D. candidates from around the world. Among the scholarship recipients to date are three international researchers from Vietnam, Iran, and China, all of whom are women—an encouraging example of the diverse and high-potential talent that thrives within the KABFI program.

We extend our sincere thanks to Landesbank Baden-Württemberg (LBBW) for its generous financial support of doctoral scholarships, and to Deutsche Börse Group for its invaluable contribution of historical and real-time order-book data covering all assets traded on Xetra, Eurex, and EEX—critical infrastructure for advanced research in capital markets and financial data science.

KABFI offers companies a unique opportunity to engage with a research environment that combines academic rigor, data-intensive methods, and relevance to real-world business challenges. Through interdisciplinary research at the intersection of finance, AI, and data science, our Ph.D. researchers bring fresh insights to both applied and theoretical questions.

Our alumni regularly attract competitive offers from global financial hubs such as New York, London, and Zurich. We therefore particularly encourage local and national industry partners to engage early—by sponsoring scholarships, supporting research infrastructure (e.g., datasets), or offering part-time roles to KABFI students. These collaborations increase the likelihood of recruiting highly sought-after talent before they enter international job markets and help ensure that top-tier quantitative researchers remain connected to the German innovation ecosystem.

We invite you to consider the following forms of collaboration:

• Sponsoring Scholarships
  Help foster the next generation of researchers in AI and data-driven business applications. Corporate sponsorships enable outstanding candidates to pursue their Ph.D. at KIT. Regular updates and mutual exchange ensure that research insights remain aligned with evolving business needs and innovation priorities.

• Developing In-House Talent
  Selected corporate partners have chosen to support their own employees in pursuing a Ph.D. within the KABFI framework—typically through a 50% employment model. This format allows participants to remain professionally engaged while contributing to academically rigorous and industry-relevant research.

• Offering Part-Time Opportunities to KABFI Scholars
  We welcome collaboration with companies interested in offering research-oriented part-time employment to KABFI doctoral candidates. These partnerships provide valuable practical experience for the students and bring fresh, data-driven perspectives into your organization.

Whether your goals lie in advancing in-house expertise, sponsoring applied research, or engaging with emerging academic talent, we would be pleased to explore a tailored collaboration with your organization.
Please contact us at kabfi∂fbv.kit.edu to start the conversation.