Difference between revisions of "MSCA Network"
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== EIT Digital == | == EIT Digital == | ||
== Fraunhofer Institute == | == Fraunhofer Institute == | ||
| − | = European Central = | + | |
| + | * Role: Contribute data to the project, contribute their expertise in enabling efficient and sustainable transfer of scientific knowledge into commercial use. Exposure to a world-leading applied research environment. (see Table 1.2.a GA) | ||
| + | |||
| + | === Course/workshops === | ||
| + | * Advanced course: Multi-Criteria Decision Making in Sustainable Finance (M30, 3 ECTS): 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 | ||
| + | * Lab Training: Virtual reality lab, optimization lab, computer graphics and visualisation lab, analytics lab | ||
| + | |||
| + | === DC secondments === | ||
| + | * [https://wiki.fin-ai.eu/index.php/MSCA_Individual_Research_Projects#Industry_standard_for_blockchain DC3: Industry standard for blockchain], WP4, M12 – M15: applied industry-research, contribute to multiple projects on blockchain and decentralized finance | ||
| + | * [https://wiki.fin-ai.eu/index.php/MSCA_Individual_Research_Projects#Collaborative_learning_across_data_silos DC6: Collaborative learning across data silos], WP1, M12 - M15, applied industry-research, implement various use-cases | ||
| + | *[https://wiki.fin-ai.eu/index.php/MSCA_Individual_Research_Projects#Risk_index_for_cryptos DC7: Risk index for cryptos], WP4, M27 – M30, exposure to world-leading research centre and infrastructure, implementing a prototype solution | ||
| + | *[https://wiki.fin-ai.eu/index.php/MSCA_Individual_Research_Projects#Audience-dependent_explanations DC9: Audience-dependent explanations], WP3, M27 – M30, improve know-how transfer by using and implementing advanced financial models | ||
| + | *[https://wiki.fin-ai.eu/index.php/MSCA_Individual_Research_Projects#Deep_Generation_of_Financial_Time_Series DC15: Deep Generation of Financial Time Series], WP1, M18 – M21, applied industry-research, implementing several use-cases | ||
| + | *[https://wiki.fin-ai.eu/index.php/MSCA_Individual_Research_Projects#Investigating_the_utility_of_classical_XAI_methods_in_financial_time_series DC16: Investigating the utility of classical XAI methods in financial time series], WP3, M18 – M21, Research needs to be validated with industry to achieve the envisioned impact | ||
| + | *[https://wiki.fin-ai.eu/index.php/MSCA_Individual_Research_Projects#Fair_Algorithmic_Design_and_Portfolio_Optimization_under_Sustainability_Concerns DC17: Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns], WP3, M18 – M21, for training in portfolio optimization in the presence of sustainability scenarios | ||
| + | |||
| + | (taken from Table 3.1.c GA) | ||
| + | |||
| + | = European Central Bank = | ||
Latest revision as of 18:38, 26 October 2023
Universities
University of Twente
WU Vienna
HU Berlin
Bucharest University of Economic Studies
Babes-Bolyai University
Bern Business School
Kaunas University of Technology
University of Naples
Industrial Partners
Deloitte
Swedbank
Intesa Sanpaolo
Raiffeisen Bank
Cardo AI
Royalton Partners
International Research Centres
ARC Greece
EIT Digital
Fraunhofer Institute
- Role: Contribute data to the project, contribute their expertise in enabling efficient and sustainable transfer of scientific knowledge into commercial use. Exposure to a world-leading applied research environment. (see Table 1.2.a GA)
Course/workshops
- Advanced course: Multi-Criteria Decision Making in Sustainable Finance (M30, 3 ECTS): 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
- Lab Training: Virtual reality lab, optimization lab, computer graphics and visualisation lab, analytics lab
DC secondments
- DC3: Industry standard for blockchain, WP4, M12 – M15: applied industry-research, contribute to multiple projects on blockchain and decentralized finance
- DC6: Collaborative learning across data silos, WP1, M12 - M15, applied industry-research, implement various use-cases
- DC7: Risk index for cryptos, WP4, M27 – M30, exposure to world-leading research centre and infrastructure, implementing a prototype solution
- DC9: Audience-dependent explanations, WP3, M27 – M30, improve know-how transfer by using and implementing advanced financial models
- DC15: Deep Generation of Financial Time Series, WP1, M18 – M21, applied industry-research, implementing several use-cases
- DC16: Investigating the utility of classical XAI methods in financial time series, WP3, M18 – M21, Research needs to be validated with industry to achieve the envisioned impact
- DC17: Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns, WP3, M18 – M21, for training in portfolio optimization in the presence of sustainability scenarios
(taken from Table 3.1.c GA)