Career
Upcoming: May 2026
About
Crypto markets challenge many assumptions of traditional finance. This workshop examines whether digital assets can be rigorously modeled using quantitative methods, real data, and scientific reasoning.Participants will explore crypto market microstructure, on-chain behavioural signals, algorithmic trading, market-making, risk management, and portfolio optimisation.
Through hands-on sessions with real tokens, on-chain analytics, and live trading infrastructure, the workshop connects academic theory with how crypto markets operate in practice.Topics include fragmented order books across CEXs and DEXs, market inefficiencies, behavioural dynamics, on-chain intelligence, execution in high-volatility markets, and the limits of hype-driven narratives.
The half-day workshop concludes with a critical discussion on FOMO, misinformation, and how to identify meaningful research opportunities in crypto.
Target audience: MSc students in quantitative finance, data science, economics, and related fields.
External Professionals: Registration Fee: 500 CHF. Please use the registration link below.
When: Thursday, May 28, 2026, 14.00 to 19.00, Room: ML H 37.1
Registration
- use this link
- Places are limited
- ETH Students (BSc, MSc, PhD) free of charge
- ETH FinsureTech Alumni (CAS) free of charge
- External Professionals: 500 CHF (invoice)
Lecturer: Mauricio Gonzales, PhD
- Mauricio is the Founder & CEO of Euler Advisory, a firm specialising in quantitative advisory for TradFi and digital assets. He holds a PhD in Applied Mathematics, jointly managed by CNRS and ENS under a European Research Council (ERC) excellence project. Previously, he spent over four years as Head of Model Validation at an EU Credit & ESG Rating Agency, overseeing all ESMA-compliant risk models. His institutional risk expertise is complemented by his practical market experience as a former Business Development Manager at Bitget (CEX).
Guest Speakers:
- Julien Wanecque, Timechain Capital - Co-founder and Managing Partner of UAP General Partner, a management company registered with the CSSF in Luxembourg, through which he manages Timechain Capital, an alternative investment fund specialising in digital assets.
- Tarmo Ploom, PhD, ChainAware - Tarmo is Co-Founder of ChainAware.ai - a web3 predictive analytics company. Tarmo is OpenGroup Distinguished Architect, he worked before as Chief Architect of Finova, before this 10+ years as VP for Global Integration Architecture of Credit Suisse. He is a CFA, CAIA, has PhD from Max Planck Institute in Munich and some additional degrees.
- Martin Ploom, ChainAware - Martin is Co-Founder of ChainAware.ai - a Web3 predictive analytics company. Martin worked before as VP 10 years for Credit Suisse in Zurich, and before for Man Investments. He has many degrees + CFA.
Agenda:
- Order book fragmentation across
- CEX and DEX Market microstructure anomalies
- Volatility clusters & regime shifts in digital assets
- Behavioural drivers
- Why crypto is an ideal quant playground
- Token Ranking System
- Behavioural segmentation
- Fraud detection & rug-pull probabilities
- Why Twitter hype is meaningless
- How on-chain data reveals real community intention
- Applications for portfolio construction
Practical Session: On chain data using ChainAware
- Market-making strategies in fragmented markets
- Execution algorithms adapted to high-volatility assets
- Backtesting infrastructure
- Event-driven trading
- How on-chain and off-chain signals interact
- Design constraints for crypto HFTBuilding stable systems in 24/7 markets
Practical Session: Live Execution Demo using Bitget
- Why Gaussian assumptions fail in crypto
- Volatility & risk modelling
- Value-at-Risk in crypto
- Position sizing & Stop-loss logic
- Long-term fund stability
- Multi-factor signals combining on-chain & off-chain data
- Risk-adjusted optimisation of high-volatility assets
- Regime detection
- Transaction cost–aware rebalancing
- Case studies on token selection
- Long-term portfolio drift under market cycles
Practical Session: How to construct and rebalance portfolios using quantitative signals
- How FOMO is engineered
- How bots amplify fake sentiment
- Why token narratives fail under data scrutiny
- Red flags are visible only on-chain
- How to distinguish real research from marketing
- How to translate “community health” into quantitative metrics
Career Opportunities
external page Millennium Management: (Intern) Quantitative Researcher, Systematic Equities
Millennium is a top tier global hedge fund with a strong commitment to leveraging market innovations in technology and data to deliver high-quality returns.
A small, collaborative, and entrepreneurial systematic investment team is seeking a strong equities quantitative researcher to join in developing new signals and strategies. This opportunity provides a dynamic and fast-paced environment with excellent opportunities for career growth.
Job Description
We are looking for a Junior Quantitative Researcher as part of a small team with a focus on systematic equity strategies.
Preferred Location: Zug, Switzerland
Principal Responsibilities:
- Work alongside the Senior Portfolio Manager on alpha research, with a primary focus on: idea generation, data gathering, research/analysis and back testing systematic equity strategies
- Research ML or NLP techniques for alpha generation (depending on the candidate’s background)
- Combine sound financial insights and statistical learning techniques to explore, analyze, and harness a large variety of datasets in order to build predictive models that will have a direct impact on the investment process
Preferred Technical Skills:
- Strong research and programming skills
- Demonstrated experience programming in Python
- Bachelors, Masters or PhD degree in a quantitative subject such as Data Science, Computer Science, Statistics, Applied Mathematics or related fieldo
- An academic background in machine learning or NLP is a plus
Preferred Experience:
- Demonstrated interest in finance through prior internship experience or independent projects (please specify on CV).
- Strong economic intuition and understanding of financial markets, particularly equities
- Creativity and critical thinking
Target Start Date: Open to Candidates who can start by 9/2026
Next Steps: Please send all resume submissions to , including a recent transcript with courses and grades achieved.
Past Events 2025
The ETH – ADIA Lab Collaboration & ADIA Lab Data Science Competition 2025
Date & Time: Friday, May 16, 2025, 14.00 - 18.00 (Zurich Time)
Location: ETH Zurich Main Building E 5
Registration: closed
Agenda:
14.00 Welcome
14.05 “ADIA Lab’s Research Areas & Opportunities for Students” Dr. Horst Simon & Prof. Marcos Lopez de Prado, ADIA Lab (partly remote)
14.30 “The Strategy Assembly Line – How ML can be used when developing an Investment Strategy”, Prof. Marcos Lopez de Prado, ADIA Lab (remote)
15.00“Insights on Courses and ongoing Student Projects with ADIA Lab” – Dr. Bastian Bergmann, ETH Zurich & Dr. Koushik Balasubramanian, ADIA Lab
15.20 “Brainstorming Session on Future Student Projects”, All
15.45 Break
16.15 “ADIA Lab Data Science Competition 2025” Dr. Emanuele Olivetti, ADIA Lab & Jean Herelle, CrunchDAO
17.30 Networking & Drinks
Speakers from ADIA Lab
- Prof. Marcos Lopez de Prado, Global Head, Quantitative R&D, ADIA Lab
- Jean Herelle, Founder, CEO CrunchLab
- Dr. Horst Simon, ADIA Lab,
- Dr. Koushik Balasubramanian, ADIA Lab
Career Talk: "Quantitative Research at Millennium"
Date: 4 June 2025, 17.15 – 18.30 Followed by Apero
Location: ETH Main Building, Room E 41
Please register here: Link
About: Hear from Millennium senior leadership about trading, technology, and quantitative research in a hedge fund. In this unique engagement, you will have the chance to learn about the ins and outs of quantitative researchers and how they fit into the day-to-day workings of the industry. Following the discussion, you will also have the chance to network with three of Millennium’s key leaders.
Speakers:
- Yves Guntern - CEO, Millennium Switzerland
- Pranat Pathak - CIO, EMEA and Global Head of Fixed Income, Commodities & Risk Technology
- Emmanuel Lanzmann – Global Head of Quantitative Modeling and Technology – Fixed Income, Commodities & Risk Technology
In collaboration with external page Analytics Club at ETH
CAS ML in Finance & Insurance Innovation Project
About
Together with the ETH Analytics Club we are launching a pilot initiative that brings together professionals from the CAS in Machine learning in Finance and Insurance and ETH MSc students for an innovation project focused on Machine Learning in Finance and Insurance.
Get ready to apply your expertise in a unique pilot project focused on Machine Learning within the Finance and Insurance sectors.
Time: August/September to October 2025
Target Group: ETH MSc Students
This is your chance to actively contribute to a cutting-edge project, gaining invaluable practical experience, deep industry insights, and expanding your professional network within ML in Finance and Insurance.
The pilot project will not offer any ECTS or any other form of formal recognition. You will get a certificate of participation.
Ideal MSc Students:
- Interest in Finance and Insurance Topics
- Background in STEM subjects or related
- Experience with Machine Learning tools in a programming environment eg. through class projects
- Hardworking, detail-oriented, collaborative
If you are interested to receive more details and the project proposals, pre-register via Email to .
In collaboration with external page Analytics Club at ETH
About
Join us for an exclusive, hands-on trading simulation workshop and get a real feel for the fast-paced world of trading.
It's your chance to step into the shoes of a professional trader. You'll get to apply trading strategies in real-time using a state-of-the-art simulation from AmplifyMe. Beyond the challenge, you'll have the unique opportunity to network with industry professionals.
Learn about different career paths, gain insights into what it takes to succeed, and get your questions answered directly.
Date: Wednesday, September 10, 2025
Time: 09.15 - 13.00
Location: ETH Zurich Main Building, New Room D 16.2
Open for: ETH & UZH Students (places limited)
Registration: (closed)
Agenda:
- 09:15 - Arrival
- 09.30 - MLP Welcome (Yves Guntern, CEO Switzerland)
- 09.45 - Millennium Campus Programmes (Emily Glaister)
- 10.00 - Trading Simulation Challenge (AmplifyMe)
- 12.00 - Sandwich Lunch & Networking
- 13.00 - Closing
Don't miss this chance to boost your skills, expand your network, and explore your future in finance
In collaboration with external page Analytics Club at ETH
About
This workshop shows how to turn high-quality macroeconomic data into practical trading strategies. In collaboration with JP Morgan, Macrosynergy combines hedge fund experience with advanced data solutions to give participants a real edge in investment strategy design.
Key takeaways from the workshop:
- Why traditional datasets fall short – understand the risks of revisions, lack of release dates, and methodological inconsistencies.
- Quantamental data as the solution – learn how point-in-time, fully documented indicators enable robust signal generation and backtesting.
- JPMaQS in practice – explore coverage, key indicators, and integration with return series for strategy development.
- Step-by-step strategy process – move from hypothesis formulation and signal construction to backtesting, performance evaluation, and enhancement.Practical applications – see how macro hedge funds, asset managers, and policymakers use these tools to inform decisions and create investment-ready strategies.
By the end of the session, participants will know how to build and validate strategies that combine sound economic reasoning with reliable, cutting-edge data.
When: October 17, 2026, 15.00 to 18.00, Room: HG D 1.1
Agenda:
Part 1: Presentation
- Why Traditional Economic Time Series Fall Short
- The Case for Independent Point-in-Time Data. Risks of using non-vintage or self-maintained indicators
- Quantamental database as solution to these challenges
- Who Needs High-Quality Quantamental Data?
- JPMaQS in Practice
Part 2: Hands-on Part - How to create a trading strategy?
- Hypothesis Formulation, Signal Construction, Empirical Evaluation
- Assess: Predictive significance, forecast accuracy, trading value (Sharpe, turnover, drawdowns)
- Enhancement & Validation
- Notebook Session: Cross-country equity risk allocation with statistical learning
Registration: (closed)
In collaboration with external page Analytics Club at ETH
Past Events - 2024
Past Event: Nov 5, 2024 with Qube Research Technologies
Title: What Are Quant Researchers Doing in Hedge Funds?
Speaker: Adrien Hardy, Quantitative Research Director, Qube Research Technologies
Date: Nov. 5, 2024, 18.15-19.00 +Q&A,
Location: ETH Zurich, ML Building Room D 28
(no registration required)
About: In this talk, I'll start with a quick overview of quantitative trading in the hedge fund industry. Then, I'll cover some of the key technical challenges that quant researchers face in their day-to-day work. This will give you a sense of the types of mathematical and machine learning problems quants are trying to solve.
Title: "Day in the Life at Millennium"
Date & Time: Wednesday, Dec 4; 12.15 to 13.15 (Zurich time) online via Zoom
Zoom Link: Please write a mail to
Speakers:
- Harry Sairaman (Senior Portfolio Manager),
- Jules Lopvet (Quantitative Researcher) &
- Emily Glaister (EMEA Campus Recruitment Lead)
About: Invitation for ETH students to hear from Millennium professionals offering an insight into life at the company, their careers in Finance and the internship roles / job opportunities available.
Past Career Opportunities
external page Millennium Management: Quantitative Research Summer Intern (Zug)
Job Responsibilities
- Explore and analyze a vast array of datasets, including both market data from asset trading and alternative data from other aspects of the economy using machine learning/statistical/applied math/econometric techniques
- Apply statistical and machine learning techniques to test investment ideas
- Collaborate with portfolio managers, quantitative researchers, and software engineers to deploy these investment ideas and make an impact on the team’s PnL
- Analyze financial data and develop predictive models to support decision making within the team
- Implement and develop machine learning algorithms to be leveraged in the investment process
- Collaborate with the team to identify opportunities for process improvement and innovation
Qualifications, Skills and Requirements
- Pursuing a master’s degree in a technical or quantitative discipline; such as Financial Engineering, Quantitative and Computational Finance, statistics, mathematics, physics, or computer science graduating in 2025
- Demonstrated proficiency in at least C++ or Python
- Experience performing an in-depth research project examining real-world data
Details and application external page here