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”:
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- CV with a list of your publications
- Research statement (1-2 pages) that describes your prior research and future research plans
- 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:
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 spring semester. (Last update: 05.11.2024) 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: Tissue Structure Reconstruction Using Generative Models
Keywords: single-cell spatial transcriptomics, generative models, point cloud generation
Single-cell RNA sequencing (scRNA-seq) technologies have enabled high-throughput profiling of cellular transcriptomes, revealing unique transcriptional profiles within individual cells and advancing our understanding of cellular diversity. Despite this progress, a key limitation of scRNA-seq is the loss of spatial context, which is important for understanding how cells interact with their environment. Spatially resolved sequencing technologies aim to address this issue, but they are constrained by the number of genes that can be measured. Therefore, we target to develop a generative model that reconstructs spatial locations of cells from their gene expressions by learning spatial priors over existing spatial transcriptomics datasets.
Requirement: We are looking for students with experience in deep learning, especially generative models. Basic knowledge of single-cell biology is a plus but not required. Applicants should be experienced with Python (Programming Language) and Pytorch (deep learning framework).
Level: Master
Contact: Yist YU (tingyang.yu@epfl.ch)
- Project 2: Multi-target Open-World Semi-Supervised Learning
Keywords: open-world semi-supervised learning
In many real-world applications, unlabeled test data contains new classes that have not been previously encountered in the labeled training data. In such scenarios, machine learning algorithms need to annotate samples from seen classes but also discover novel classes that have not been previously seen in a labeled dataset, thus overcoming the categorical shift between labeled and unlabeled data. However, the current works focus on the condition that there is only one unlabeled dataset. The goal of the project is to extend the problem setting by considering multiple unlabeled datasets. We aim to propose a method that can perform open-world SSL on different unlabeled datasets.
Requirement: We are looking for students with experience in deep learning and with high interest and self-motivation to develop novel machine learning methods. Applicants should be experienced with Python (Programming Language) and Pytorch (deep learning framework).
Level: Master
Contact: Shuo Wen (shuo.wen@epfl.ch)
- Project 3: Weak-to-Strong Generalization
Keywords: AI alignment, superhuman model, representation learning
Human preference fine-tuning techniques, such as reinforcement learning with human feedback (RLHF) and direct preference optimization, are essential for developing human-level AI systems (e.g., ChatGPT). These approaches rely on the assumption that human annotations are of high quality. However, creating superhuman models in this framework presents a challenge: we need annotations for highly complex data that may exceed human comprehension. Since this is practically intractable, we must develop a new learning paradigm—namely, weak-to-strong generalization. In this approach, a weak model helps elicit the capabilities of a stronger model. Specifically, we focus on developing techniques to train well-per-trained large models using weak (or imperfect) supervision.
Requirement: We seek students with strong programming skills to assist with various tasks, particularly those involving numerous AI-related experiments.
Level: Master
Contact: Myeongho Jeon (myeongho.jeon@epfl.ch)