Comprehensive doctoral training with local courses, network events, advanced courses, and transferable skills
Each recruited researcher will develop new scientific knowledge and skills through conducting original research under their IRPs. Researchers will need to follow and pass five mandatory, foundation courses (local training). DCs will have mandatory network training events. All network training events will feature presentations from DCs.
Each DC will be assigned a combination of at least three elective, advanced courses (made accessible to a wider audience), tailored to fellow's experience, IRP and career plans. Many new modules will be created at the intersection of Finance, data science, AI, ML, explainability, blockchain and sustainability which are not available in existing Finance PhD programs.
DCs will undergo a tailored transferable skills development program, resorting to courses that will be organised mostly via joint industry-academia courses. Transferable skill training is aligned with the 2018 Eurodoc report for early-stage researchers.
Each DC will spend 18 months in industry and up to 6 months at one of the world-leading research centres (European Central Bank, ARC, Fraunhofer Institute). DCs will significantly expand their experience and skills by learning how Digital Finance is implemented both through traditional financial intermediaries across Europe (DBA, RAI, SWE) and innovative Fintechs (ROY, CAR).
Foundation courses delivered at partner institutions.
| # | Course | WP | Description | ECTS | Month |
|---|---|---|---|---|---|
| 1 | Foundation of data science (BBU) | WP1 | Introduction to a range of topics and concepts related to the data science process. It will cover the technical pipeline from data collection, to processing, analysis and visualisation. | 4 | M12 |
| 2 | Introduction to AI for financial applications (WWU) | WP2 | Getting started with ML; Introduction to supervised and unsupervised learning; deep learning and reinforcement learning. Explore how to use these methods for financial applications (financial forecasting, credit risk, etc.) | 4 | M12 |
| 3 | The need for eXplainable AI: methods and applications in finance (BFH) | WP3 | Introduction to XAI methods; state-of-art models (LIME, SHAP, DiCE, LRP, counterfactual explanations, etc.). Challenges of classical methods. Introduction to methods suited for financial applications. | 4 | M12 |
| 4 | Introduction to Blockchain applications in finance (ASE) | WP4 | Introduction to the blockchain technology, concepts such as mining, hashing, proof-of-work, public key cryptography, and the double-spend problem. Overview of the design principles and challenges. | 4 | M18 |
| 5 | Sustainable finance (UNA) | WP5 | Introduction to sustainable finance strategies. Overview of how these strategies can minimise organisational risk, create long-term business value, and improve social and environmental impact. | 4 | M18 |
| 6 | Ethics applicable to digital aspects (UTW) | WP9 | Introduction to Ethical Artificial Intelligence, with a specific focus on digital aspects. | 4 | M36 |
Events bringing the entire network together.
| # | Event | Lead | Description | ECTS | Month |
|---|---|---|---|---|---|
| 1 | Kickoff Meeting - UTW (NL) | UTW | Kickoff Meeting and Technical Training. Fellows, academic- and industrial supervisors, and representatives from associated partners. Visits to the Finance Lab of the university will be arranged, where researchers will be offered several training sessions like 'Introduction to Digital Finance', 'Blockchains in Digital Finance', and 'Ethical AI in Finance' by the UT faculty and additional consortium members. | 3 | M12 |
| 2 | Orientation Training Digital Finance - WU Vienna (AT) | WWU | Doctoral training combined with COST Action meeting. This meeting will be organised in conjunction with a COST Action meeting, enabling fellows to meet European researchers in Digital Finance outside the doctoral network. The meeting will be preceded by a general five-day training course in Digital Finance. | 3 | M15 |
| 3 | Industrial Doctoral School on FinTech - EIT Digital (ES) | EIT | Open summer school hosted by EIT Digital. EIT Digital will organise an open summer school in Madrid, tailored to doctoral candidates in FinTech. Fellows will be able to interact with doctoral candidates in digital finance from outside the network. The theme of the summer school is 'Disrupting Finance with Digital Technologies'. | 4 | M20 |
| 4 | Regulation in Digital Finance Workshop - ECB (EU) | ECB | Doctoral training combined with ECB site visit. The workshop includes site visits to the ECB, providing direct immersion into the regulatory aspects of digital finance, including topics such as AI bias, data sovereignty and digital currencies. Fellows will attend a four-day training program. Two days are allotted to the topic of 'Regulatory aspects of digital finance', conducted by regulatory experts from ECB. | 2 | M24 |
| 5 | Mid-term Review Event - BBU (RO) | BBU | Fellows, academic- and industrial supervisors, and representatives from associated partners. The mid-term review event is a key event for the DIGITAL network that will bring together researchers, industry, regulators and supervisors. The training component of the meeting is a short course on 'Sustainability in Digital Finance'. | 1 | M30 |
| 6 | Digital Finance Industry Event - UNA (IT) | UNA | Hosting industrial partners from within and outside the doctoral network. Fellows will intensively interact with industry and orient themselves on potential digital finance careers outside academia. Industrial partners from both within the network (CAR) and outside will provide training on: 'Fraud detection in digital accounting', 'Responsible AI in finance' and 'Sustainable digital finance'. | 2 | M36 |
| 7 | Training & Development Workshop - KUT (LT) | KUT | Fellows, academic- and industrial supervisors, and representatives from associated partners. In addition to an open conference on the topic of ML for Option Pricing, the event will also include content training on 'Designing digital finance tools' and a transferable skills training on 'Entrepreneurship in digital finance'. | 2 | M40 |
| 8 | Closing Conference - UTW (NL) | UTW | Fellows, academic- and industrial supervisors, and representatives from associated partners. A selection of renowned keynote speakers from both academia and industry will speak at plenary sessions. Fellows will also have the chance to speak with principal scientists and industrial partners, reflecting on their work. The best project of the doctoral network, selected by the advisory board, will receive a Best Doctoral Research award. | 2 | M48 |
Specialized courses from across the consortium. Type: E=External, N=Network.
| # | Course | WP | Type | Lead | Description | ECTS | Month |
|---|---|---|---|---|---|---|---|
| 1 | Synthetic Data Generation for Finance | WP1 | N | ARC | Use of deep learning techniques (e.g., Generative Adversarial Networks) to generate synthetic financial data indistinguishable from real data. Use cases for synthetic data in AI training, e.g., fraud detection, crisis simulation. | 4 | M12 |
| 2 | Anomaly Detection in Big Data | WP1 | E,N | BBU | Principles to detect anomalies. Discuss ways of handling data errors (e.g., human inspection, removing outliers, deploying AI to fill in gaps in data). Mapping of data quality. | 4 | M18 |
| 3 | NLP with Transformers | WP1 | E,N | ARC | Combine computational linguistics and role-based modelling of human language with statistical machine learning and deep learning models. Understand to use the most advanced transformers to perform advanced tasks. | 4 | M24 |
| 4 | Dependence Structures in High Frequency Data | WP1 | N | ASE | Automatic detection of dependencies between arbitrary numbers of vectors. Techniques for identifying patterns such as time-dependent trends, volatility clustering, seasonality, and fat tails. Application of copulas and spectral measures. | 3 | M30 |
| 5 | Reinforcement Learning in Digital Finance | WP2 | N | UTW | Selection of learning algorithms relevant to digital finance applications. Deploying RL for decision-making in areas such as trading, risk management, and fraud detection. | 4 | M12 |
| 6 | Machine Learning in Industry | WP2 | N | CAR | Principles of machine learning in industry. Business assessment of automation decisions. Practical implications of machine learning. Availability and costs of high-quality data. | 4 | M18 |
| 7 | Deep Learning for Finance | WP2 | E,N | BBU | Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning. Analyse variance for DL applications. | 3 | M24 |
| 8 | Data-Centric AI | WP2 | N | WWU | Empower SMEs in digital finance to deploy AI with limited datasets. Construct high-quality samples to maximise training impact. Identify weak spots in data quality. | 3 | M30 |
| 9 | Cybersecurity in Digital Finance | WP3 | N | UTW | Cybersecurity from perspectives of social behaviour, software and hardware. Security of cloud services and compliance with EU regulations. Detecting and preventing fraud. | 3 | M12 |
| 10 | AI Design in Digital Finance | WP3 | N | ASE | Overview of contemporary AI techniques in digital finance. Designing impactful AI with explicit consideration for energy consumption, bias, explainability, and fairness. | 4 | M18 |
| 11 | Barriers in Digital Finance Adoption | WP3 | N | WWU | Hurdles for society-wide adoption of digital finance. Design principles to include genders, minorities and vulnerable groups. Fast-paced industry, start-up climate, competition. | 3 | M24 |
| 12 | Explainable AI in Finance | WP3 | E,N | BFH | Classification of white box- and black box models. Applicability of classical XAI techniques in finance. Audience-dependent explanations. Emerging XAI techniques. | 4 | M30 |
| 13 | Digital Finance Regulation | WP4 | E | ECB | Overview of the regulatory field in digital finance. Outlook to pending changes in EU regulations. Directions and focus points. Best practices for compliance and monitoring. | 3 | M12 |
| 14 | History and Prospects of Digital Finance | WP4 | N | UNA | Past developments in digital finance (including digital assets, algorithmic trading, AI) and trends for the next decade. Reflection on decentralisation. Reflection on AI. | 3 | M18 |
| 15 | Blockchains in Digital Finance | WP4 | E,N | ASE | Technical, financial and legislative principles of blockchain technology and its (potential) applications in digital finance. Impact of decentralised finance. | 4 | M24 |
| 16 | Digital EIT Summer School | WP5 | E,N | EIT | Disrupting Finance with Digital Technologies. Reflection on the impact of FinTech on society. Overview of latest advances. Case studies. Learning to write a business plan. | 4 | M18 |
| 17 | Green Digital Finance | WP5 | E,N | KUT | Instill awareness of energy consumption and ecological footprint of digital finance. Techniques for energy-efficient algorithm training and deployment of digital financial services. Trade-offs between performance and environmental impact. | 3 | M24 |
| 18 | Multi-Criteria Decision Making in Sustainable Finance | WP5 | E,N | FRA | Principles of multi-criteria decision making. Various techniques and concepts (e.g., fuzzy set theory, analytical hierarchy process, preference modelling) to incorporate multiple objectives, in line with ESG principles. | 3 | M30 |
Cross-cutting professional development.
| # | Skill | Providers | Sub-courses | ECTS | Month |
|---|---|---|---|---|---|
| 1 | Gender and Diversity | ECB | Gender and Diversity Dimension in Research (ECB) | 2 | M3 |
| 2 | Research and Project management | ASE, ROY, BFH, UNA | Project Management (ROY) HE framework and research project management (ASE) Research Ethics and Sustainable Research Management (BFH) Environmental Aspects (UNA) | 4 | M12 |
| 3 | Research Skills | BFH, UNA, RAI, WWU | Scientific Writing (BFH) Scientific Communication (RAI) Open Science Principles (UNA) Citizen Science (WWU) | 4 | M18 |
| 4 | Entrepreneurship | ECB, BFH, EIT | Intellectual Property Rights and Patenting (ECB) Entrepreneurship Training (EIT) Entrepreneurial Finance (BFH) Start-ups and Industry Transfer (EIT) | 4 | M24 |
| 5 | Labor Market Skills | UTW | Job Applications (UTW) Communication skills (UTW) | 2 | M36 |
| Name | Affiliation |
|---|---|
| Prof. A. Hirsa | Columbia University |
| Prof. C. Harvey | Duke University |
| Prof. O. Linton | Cambridge University |
| Prof. J. Fan | Princeton University |
| Prof. M. Dai | Hong Kong Polytechnic University |
| Prof. G. Stahl | Peking University HSBC |
| Partner A | Partner B | Degree Type |
|---|---|---|
| UTW | BBU | Double Doctoral Degree |
| ASE | UTW | Double Doctoral Degree |
| WWU | UTW | Double Doctoral Degree |
| UNA | UTW | Double Doctoral Degree |
| UNA | BBU | Double Doctoral Degree |
| EIT | All academic partners | European Master's degree in Digital Finance |