We are constantly looking for exceptional and highly motivated new team members. So don’t hesitate to send your prospective application following these guidelines.

Postdoctoral fellows

We are looking for highly motivated postdoctoral candidates to join us on a scientific journey! We invite candidates that are excited about conducting breakthrough research in machine learning and/or at the intersection of machine learning and biomedicine. The candidates should have a strong computational background. Candidates should have a Ph.D. or equivalent degree in computer science, statistics, applied math, computational biology or a closely related field. Machine learning topics we are especially focused on: domain adaptation/generalization, few-shot learning, unsupervised and semi-supervised learning, graph representation learning, explainable AI, but candidates from other areas are also welcomed to apply. Prior experience in working with biomedical data is not required but a plus.

Application:
To apply, please send the following documents to Maria with the email subject line: “Postdoctoral application”:

1. 1. CV with a list of your publications
   2. Research statement (1-2 pages) that describes your prior research and future research plans
   3. Arrange for at least two letters of reference from your previous research supervisors and collaborators to be sent to Maria

Additionally, you can check out these opportunities for fully funded postdoctoral positions. Feel free to reach out if you are interested in doing research in the lab under these fellowships:

• SNSF Swiss Postdoctoral Fellowships
• EPFL AI Center Postdoctoral Fellowship

PhD students

We are always looking for highly motivated and talented PhD students. PhD students can join the lab through EPFL’s PhD program in computer science (EDIC)  or computational biology (EDCB). The admission process in both programs is centrally handled by EPFL. The system allows you to specify labs you are interested in. ELLIS PhD students still need to be admitted to one of the EPFL’s doctoral programs.

Summer Research Interns

If you are a Bachelor’s or Master’s student, consider applying to the Summer@EPFL internship program. If you are looking for a PhD internship, send Maria an email directly.

Bachelor & Master students

How to apply for Master’s Thesis with Our Lab

If you are an EPFL master’s student interested in conducting your thesis with our lab, send Maria an email including CV and transcripts and use subject line “Master’s Thesis Application”.

How to apply for Bachelor & Master Research Projects

Please apply using the following link. If you are interested in multiple projects, please send only one application and indicate the projects you are interested in the motivation letter.

Below you can find our offerings for Bachelor & Master Research Projects for the 2025-26 spring semester. (Last update: 15.11.2025) Please, note that the list of topics below is not exclusive. In case you have other ideas or proposals, you are welcome to contact a senior member of the lab and talk about possibilities for a tailor-made topic.

Students interested in doing a project are encouraged to have a look at the Thesis & Project Guidelines, where you will gain an understanding about what can be expected of us and what we expect from students.

• Project 1: Frequent Pattern Mining for Graphs and Point Clouds

Keywords: point cloud pattern discovery, subgraph mining, graph neural network, transformers

Identifying frequent patterns in point clouds or graph motifs is essential for revealing interpretable structural properties of complex networks. Such analyses have significant applications in fields such as biology, social science, and chemistry. However, this is a fundamentally challenging problem due to its combinatorial complexity, especially when dealing with large-scale real-world graphs or point clouds. In this project, we aim to develop a scalable framework for discovering frequent patterns in both graphs and point clouds.

Requirements: We are seeking Master’s students with experience in machine learning. Basic knowledge of single-cell biology is a plus but not required. Applicants should be proficient in Python and familiar with PyTorch for deep learning.

Level: Master

Contact: Yist YU (tingyang.yu@epfl.ch)

• Project 2: Improving agents for single-cell genomics and biomedical discovery

Keywords: Biomedical Agents, Large Language Models (LLMs), Post-training of LLMs with RL

Recent advances in the reasoning capabilities of large language models (LLMs) have led to the development of agentic frameworks capable of autonomous decision-making across various scientific domains, including single-cell biology. These systems operate through iterative think-act-observe cycles: the LLM plans actions, executes them via a domain-specific environment, and integrates feedback to guide next steps. In single-cell biology, such agents act as collaborative co-scientists, formulating hypotheses, designing validation strategies, and executing complex computational workflows. However, these LLMs often lack domain-specific training before being integrated into agentic workflows. This project aims to improve their reasoning capabilities for single-cell biology tasks through post-training methodologies.

Requirement: We are seeking students with strong experience in post-training of LLMs, particularly with reinforcement learning. Experience with agentic workflows is a huge plus.

Level: Master

Contact: Siba Smarak Panigrahi (siba.panigrahi@epfl.ch)

• Project 3: Agent Learning from Active Exploration of the Environment

Keywords: LLM Agent, Reinforcement Learning

Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long horizon tasks. However, the trained agents often struggle to generalize to novel tasks or environments. To address this issue, we need agents that can actively explore the environment and efficiently adapt from trial-and-errors. Therefore, we would like to investigate the following directions in this project: 1) developing a principled strategy to incentivize exploration of LLM agents, 2) exploring learning signals derived from the experiences (such as the state transitions and intermediate feedback), instead of relying solely on the final rewards.

Requirement: We’re looking for students with experience on Large Language Models (and Reinforcement Learning).

Level: Master

Contact: Yulun Jiang (yulun.jiang@epfl.ch)

• Project 4: Coupling LLM-based Evolutionary Search with Agentic Selection

Keywords: LLM Agent, Evolutionary Algorithms, Offline Reinforcement Learning

We propose to enhance LLM-guided evolutionary search by replacing hand-crafted, heuristic selection operators with an agentic LLM-based selector. Modern approaches already use LLMs as mutation operators, leveraging domain knowledge in prompts to generate improved candidate solutions, but selection – deciding which candidate to mutate next – remains a fixed algorithmic rule. In our project, a dedicated “Selector LLM agent” will observe the full search state (current population, lineage of mutations, and the implicit search tree) and actively choose both the next candidate to mutate and tailored mutation instructions. This aims to turn evolutionary search into a fully LLM-driven decision process, potentially yielding faster convergence, better sample efficiency, and more interpretable search trajectories.

Requirement: We’re looking for students with experience on Large Language Models and strong engineering skills.

Level: Master

Contact: Artyom Gadetsky (artem.gadetskii@epfl.ch)