Designed for scale
Twig is global energy trading and asset management company. We are active in Europe, US and Japan enabling efficient power markets and renewable infrastructure to operate profitable at scale. At heart we are a technology company operating fully automated backed by data and machine learning.
Global reach
Our business areas
We believe efficient energy markets is a corner stone in the energy transition. Twig trades energy across Europe, US and Japan. We have build a trading platform from ground up, designed for AI/ML, low latency and reliability. Our operations is 100% driven by machine learning executing millions of decisions in real time every day. We trade spot energy within seconds, hours and days, make markets and allocate risks through financial contracts.
The future of energy supply is powered solar and wind production, fewer baseload resources and extensive energy storage. We operate power infrastructure like Wind, PV and BESS across Europe. We cover the entire path from asset control, forecasting and optimisation to trading and execution on both ancillary service markets and wholesale energy markets. We are building the asset management and trading platform support profitable renewable investments and reliable power 24/7.
Transforming the energy system from hydrocarbons to renewables is the biggest undertaking of the 21st century. We know the power markets, what a good grid scale energy project looks like and have beliefs about the future energy system grounded in data. Success requires large amount of capital allocated at unprecedented speed. We provide catalyst capital in partnership with investors, institutional capital and project developers to speedup the energy transistion.
Meet our team
Anders is our C++, CUDA, and high-performance compute expert making our code run fast and reliably in the wild.
Jonas holds an MA in Creative Processes & Innovation from the University of Copenhagen. Before Twig, he served as Chief Business Officer at Seaborg Technologies, leading commercialisation, project development, and regulatory approval. Prior to that, he served as Associate Partner in M&A Plus, focusing on energy, cleantech, and industry.
Raghul did his PhD in probabilistic machine learning at the University of Cambridge, and then was at Amazon. He likes creating probability models for spatiotemporal processes, such as the weather, using neural networks as the building blocks.
PhD in Physics from Harvard, on analyzing gravitational lensing in galaxies. These days, tinkering with machine learning models while human-learning about electricity markets and weather prediction.
MSc, Phd in Bioinformatics from University of Copenhagen. Previously worked on autonomous systems and large scale deep learning systems in Apple Special Project group, Cupertino and founded an AI fintech company operating on the NYSE exchange.
Niels works as a Machine Learning Scientist at twig. He has a background from Technical University of Denmark, working on scalable Bayesian deep learning. He enjoys turning advanced ML research into practical solutions, with recent work accepted at ICLR on graph representation learning.
Gabriele did his PhD in Mathematical Optimisation and Machine Learning at École Polytechnique. He previously worked as a Research Scientist at the Zuse Institute Berlin, building AI-driven optimisation methods for large-scale decision problems, from routing to forecasting. He enjoys combining deep learning with mathematical programming to push the limits of practical optimisation.
We are hiring! We need more great people to collaborate with. If you like to solve hard problems you should reach out.
Anne Kathrine holds a MSc in International Business, with a professional background as Executive Assistant for C-levels focusing on strategy and internal business development. At Twig, Anne Kathrine works across core business processes, supporting the Groups continued growth. Her focus span internal operations within compliance, day-to-day finance, audits, market access initiatives and collaboration with advisors and partners.
PhD in statistics from the University of Copenhagen. Alexander enjoys problem-solving and previously competed in the International Mathematical Olympiad, later serving as deputy leader for the Danish team. At twig, Alexander works on execution strategy to maximize the impact of advanced model predictions.
Didrik did a MSc in Applied Physics and Mathematics and a PhD in Machine Learning. During his PhD he worked on Deep Generative Modelling and collaborated with leading researchers at RIKEN and University of Amsterdam. At twig Didrik works on our machine learning models for weather and sequential data.
Casper did a phd in Deep Learning back when VGGs were cool. More recently he initiated and lead Google Brains research efforts in data driven short term weather predictions and prior to that were at Twitter Cortex and Deepmind technologies. Casper is thrilled about putting years of AI research to work in decarbonising the grid.
MSc in software engineering from Aalborg University. Domas enjoys building reliable and scalable backend systems and has spent the better part of the last decade doing so for Danish and American fintech unicorns.
Mikael did a MSc in software development at the IT University of Copenhagen and is multilingual in backend development from C++ to TypeScript. At twig Mikael expands the features of our real-time production system while keeping it fully automated
Junesoo completed his MSc in Sustainable Energy at DTU specializing in energy markets, mathematical modelling, and optimization. At Twig Junesoo works on asset optimization within energy markets to seamlessly integrating sustainable technologies into the power grid and effectively operating them within energy markets.
MSc in Computer Science from the University of Verona. In his free time he enjoys tinkering with just about anything chips can be tricked into doing: from graphics programming, to executables reverse engineering, or programming languages design. When he's not creating pull requests, he's pulling plastic rocks at the local climbing gym.
Lukas holds an MSc in Computer Science from ETH Zurich. He previously worked at Google on large-scale test selection systems to improve efficiency and reliability, and at Planet Labs developing semi-supervised deep learning for satellite data. He also conducted climate-focused ML research with NERSC, with publications at NeurIPS and ICLR workshops.
Peter holds a MSc in Mathematics and a PhD in Computer Science with a focus on classical algorithms. Peter works on problems at the intersection of mathematics and algorithm design, with experience developing solutions for revenue optimisation and low-latency data streaming systems. Outside of work, he sings in a choir and and helps coach Denmark’s national high school team for the International Mathematical Olympiad
MSc in Computer Science from University of Copenhagen. Mads likes hunting down bugs, fixing edge cases and making the code clean and correct. He has previous experience from algorithmic trading and looks forward to putting his skills to use for green energy.
MSc in Biomedical Engineering with a focus on machine learning from Technical University of Denmark. Philip is a Linux enthusiast and loves to code, primarily in Go. He is interested in building robust and fast backend systems while reducing complexity.
Noé is pursuing an MSc in Sustainable Energy Systems at DTU, focusing on solar and renewable energy solutions. He previously worked at EnergiesDev, helping companies optimise energy procurement and billing through market insights and data-driven analysis. Prior to that, he was a Solar Energy Consultant, where he conducted pre-feasibility studies and energy audits.
PhD In machine learning from the Technical University of Denmark and previously lead of ML in Tradeshift. Rasmus likes probabilistic and bayesian models, models that learn by trying to predict the future, models that can be trained end-to-end and more models.
Sakaki spent nearly three decades at Mitsubishi Corporation, working across Japan, the US, Europe, and Asia in the power and energy sector. He held multiple senior leadership roles, most recently as General Manager of the Strategy & Planning Office for the International Power Division, and previously led Mitsubishi’s Eneco operations. Sakaki has extensive experience in developing and managing large-scale power projects—both thermal and renewable—as well as building and leading international energy businesses. He enjoys working at the intersection of strategy, partnerships, and global energy markets.
Liyang is our Energy Markets expert. He did his MSc at Stanford and PhD at the University of Oxford focusing on energy economics. Most recently he worked as a PostDoc at DTU on the topics of energy and data markets. Liyang is an advocate for diversity and equal opportunity and he spends lots of time in the theatre both on and off stage.
Frederik works as a Quantitative Researcher at twig.energy, where he applies quantitative methods to energy markets. He previously built infrastructure for algorithmic trading focusing on European energy systems. Alongside this, he has taught advanced engineering mathematics at DTU Compute, covering topics like Fourier analysis and differential equations. Frederik enjoys solving hard problems and building things from scratch, from trading systems to AI-powered chess engines.
Mar holds a Master of Law (LLM), and a Masters in International Relations (MSc). With a strong background in navigating complex regulatory environments, Mar is responsible for Market Access, Compliance, and provides support in Operations.
Kate completed an integrated MSc/BSc in Physics in St Andrews, Scotland, before moving to Copenhagen, where she got her PhD in Observational Astrophysics from KU. At Twig, Kate uses ML to model electricity markets run by the local inhabitants of her favourite planet (Earth).
PhD in Machine Learning from Technical University of Denmark with a focus on graph neural networks and probabilistic models. Peter is interested in development and deployment of advanced deep learning algorithms for predicting the future. He has previously worked on statistical signal processing for wireless communication and machine learning assisted discovery of new molecules and materials.
