Faculty Position in Computer & Communication Sciences and Life Sciences
The Schools of Computer and Communication Sciences (IC) and Life Sciences (SV) at EPFL invite applications for a faculty position at the intersection of life and computational sciences. We seek candidates who investigate biological systems as inspiration for new computational approaches. The appointment could be either at the assistant professor or tenured levels.
Candidates must have an outstanding academic record, a compelling vision, demonstrated impact, and a strong commitment to excellence in teaching and mentoring students.
EPFL attracts top students from all over the world and offers competitive salaries, generous research funding and excellent research infrastructure.
Switzerland has an exceptionally high human development index and consistently ranks highly in quality of life, economic competitiveness, and innovation.
Screening will start on January 4, 2022, but applications submitted after this date will also be considered. More info here
Call for postdoctoral fellowship applications
EPFLeaders4impact
EPFLeaders4impact is a new postdoctoral fellowship programme funding talented researchers who have the ambition to provide innovative solutions to the United Nations Sustainable Development Goals. The programme will fund between 40 and 120 fellowships of a duration of 12, 18, 24, or 36 months. Granted applicants will carry out their project at EPFL and benefit from a multifaceted training programme, including a collaboration with the non-academic sector. During the programme, EPFLeaders4impact fellows will be able to take steps towards starting a company based on their own research or to have their proposed innovative solution implemented through a technology transfer to an existing company/organisation.
NOTE: For postdoc candidates that would like the CIS to support them in finding the right lab at EPFL. Please contact: Jan Kerschgens, Executive Director CIS [email protected]
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.
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.
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.
