ML4Science Course Projects Ideas Wanted
What do Mesopotamian artifacts, Tokamak legs, Climbing Route Setting, Gravitational lenses, Applause Recognition, Rooftop solar, Stroke Rehabilitation, Air Quality Prediction, Invisibility Cloaks, Twitter Astroturfing, Drone Navigation, Classical Music and Earthquakes have in common?
In the three editions so far, 214 collaborative projects have been successfully completed, across 77 different labs of EPFL and other institutions, as part of the student projects in the machine learning course CS-433

Again this year, we have around five 560 very talented and motivated master students in the course, and we are running this collaboration initiative again and hopefully even bigger.

During 2 months from November to end of December, they will do their main course project in groups of 3 – part-time.

If your hosting lab agrees, a group of 3 of our students can contribute to a real-world research application in your lab – ANY lab of any Swiss academic institution, from any diverse area of science. Do you have an interesting dataset and question available for a short interdisciplinary project, exploring ML methods for a new application? See this websitehttps://www.epfl.ch/labs/mlo/ml4science/ for a list of previous topics we hosted in the last years.

If your lab is interested, what we need from you is not much for now – just a contact address and the fact that your lab is considering hosting a group or several, so students can get in touch with you. Please fill this form to express your interest:
It could be good to already think of available datasets, suggested prediction tasks or other scientific questions, and potential corresponding evaluation metrics.

Later by November 12th, we will ask for a definitive commitment from the head of your lab, if you are willing to host a particular group(s) of your choice, or not. We will use a different google form for that later. The matching process works by students contacting you, based on their interest in the preliminary proposed topic. After you talk with the students, you can decide to commit or not to a group (before Nov 12th). If you decide to host a particular group, the professor in the end needs to give us a grade suggestion for the domain-specific merits of the finished project (all projects will be due December 23rd). The students will submit a 4 page PDF report, and the code. We will take care of the submission system, and our assistants will help assess the technical parts of each project, but we need your help for assessing the domain specific merits of the project you host. This project counts 30% to their ML course grade. The volume of work corresponds to about 2 credit points.

Senior Event Manager – Applied Machine Learning Days EPFL
The Applied Machine Learning Days is a global platform and network for AI & machine learning. Each year, the main AMLD event is held on EPFL Campus at the Swiss Tech Conference Center. The next edition of the event will be a 5-day event happening physically from March 26th to 30th, 2022.

AMLD EPFL is one of the largest machine learning & AI events in Europe, focused specifically on the applications of machine learning and AI, making it particularly interesting to industry and academia.

More info and application

Start date : As soon as possible
Term of employment : Fixed-term (CDD)
Duration : 6 months fixed-term (CDD), renewable (1year at renewal)
Remark : Only candidates who applied through EPFL website or our partner Jobup’s website will be considered. Files sent by agencies without a mandate will not be taken into account.
Reference : Job Nb 2020
Research, PhD and Postdoc: ELISE Mobility Programs
1. Mobility program for experienced researchers

The goal of the ELISE Mobility Program for Experienced Researchers is to bring ELISE/ELLIS Fellows, Scholars, and Members together by supporting their short and long-term scientific visits to initiate collaboration within the ELISE/ELLIS community. 
More information and application

2. Mobility program for phds and postdocs

The PhD and Postdoc mobility program is aimed at existing PhD students and postdocs in the network who want to initiate a collaboration with a Fellow or Member at another site. Young researchers can give their careers a head start by gaining international experience and exchanging ideas with the best labs in Europe. The Mobility Award covers travel costs of up to 2,500 EUR per researcher over the duration of their PhD or postdoc.

More information and application
Student Projects: CIS Intelligent Assistive Robotics
A series of student projects associated to the CIS Collaboration Grant on Intelligent Assistive Robotics are now open! 
These projects aim to address additional research topics within the project or to implement related technological bricks. Several labs and research groups are typically involved in the supervision of these project, which allows students to gain multidisciplinary experience in research and development.

Check out the different projects and opportunities
Master Student Projects: CIS AI for medicine
Interpretable Deep Learning towards cardiovascular disease prediction

Cardiovascular disease (CVD) is the leading cause of death in most European countries and is responsible for more than one in three of all potential years of life lost. Myocardial ischemia and infarction are most often the result of obstructive coronary artery disease (CAD), and their early detection is of prime importance. This could be developed based on data such as coronary angiography (CA), which is an X-ray based imaging technique used to assess the coronary arteries. However, such prediction is a non-trivial task, as i) data is typically noisy and of small volume, and ii) CVDs typically result from the complex interplay of local and systemic factors ranging from cellular signaling to vascular wall histology and fluid hemodynamics. The goal of this project is to apply advanced machine learning techniques, and in particular deep learning, in order to detect culprit lesions from CA images, and eventually predict myocardial infarction. Incorporating domain specific constraints to existing learning algorithms might be needed.

More Info and application