Postdoc position : “Edge AI Devices for DigiPredict, Exposome and Beyond”

  • EPFL
  • Lausanne, VD, Switzerland
  • 11/06/2021
Full time Data Science Data Analytics Big Data Data Management Statistics

Job Description

The Ecole Polytechnique Fédérale de Lausanne (EPFL) is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs 6,000 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of 16,000 people, including over 10,000 students and 3,500 researchers from 120 different countries.
The EPFL Center for Intelligent Systems (CIS) acts as a research promotion platform for bringing together experts in machine learning, data science, computer vision, cyberphysical systems, and robotics. CIS is a joint initiative of the schools of Architecture, Civil and Environmental Engineering (ENAC), Computer and Communication Sciences (IC), Basic Sciences (SB), Engineering (STI) and Life Sciences (SV). CIS’ unique mission is to connect and support all EPFL researchers working in fields related to intelligent systems. These fields are developing technologies that, when brought together, can be used to construct intelligent systems capable of making complex, nuanced decisions in challenging, dynamic environments.

Postdoc position : “Edge AI Devices for DigiPredict, Exposome and Beyond”

Your mission :
The successful candidate will lead innovative research projects in a stimulating, open, and international research environment with a highly talented and motivated team, embedded in a strong network of academic and industrial collaborators.
The candidate will work in collaboration with 3 labs on program co-funded by the EPFL Center for Intelligent Systems (CIS):

David Atienza (STI), head of the Embedded Systems Laboratory (ESL),
Adrian Ionescu (STI), head of The Nanoelectronic Devices Laboratory (NANOLAB),
Martin Jaggi (IC), head of Machine Learning and Optimization Laboratory (MLO).

This Postdoc will be affiliated
by to two of these EPFL laboratories (NANOLAB and ESL), and will work in cooperation with additional international medical partners. The topic to be developed in this post-doctoral position is multidisciplinary and include the linking between Nanolab and ESL through the development of edge AI systems from the software mapping and architecture to the inclusion of new sensors and technologies.

This collaboration includes four different research aspects, namely:

  • Develop a biocompatible heterogeneous system integration platform for patch integration including new specialized (multi-core) edge AI computing architectures with new innovative biosensors (e.g. based on nanotechnology or metamaterials).
  • Develop new approaches to reduce the volume of information to transfer among autonomous wireless wearable sensors for real-time multi-analyte (biomarker) sensing and multiple physiological signals.
  • Develop a framework to embed federated and the new decentralized AI algorithms developed within the work of the other postdoc, on the new edge platform as well as on mobile coordinator hubs for real time usage of Digital Twins in the context of the exposome application. In principle, the framework can be retargeted to other applications beyond the field of healthcare that requires multiple types of sensors.

Our target, in addition to top publications, is to produce a template of an open-source multi-core platform, as well as an open-source mapping and optimization framework for different types of federated and AI algorithms on multi-core embedded platforms.In particular, this post-doc position
will work on the development and coordination of the following topics between Nanolab and ESL:
(i) design a multi-modal sensor interface to provide real-time data fusion from both biosensors and physiological sensors, structured for data analytics (this interface should be able to integrate also exposome data from environmental sensors at longer term),
(ii) fuse and filter in an energy-efficient way at the edge level information from multiple types of sensors, as well as on mapping federated AI algorithms for mobile and edge AI devices to enable predictive and personalized edge AI-deployed digital twins.

Your profile :
Candidates should have completed, or be near completion of a PhD with a strong international publication record in areas such as (but not limited to) sensors design, and embedded and edge AI systems architectures. Knowledge on machine learning and algorithms mapping on computing systems would be a clear plus.
Ability and motivation to co-lead applied (interdisciplinary) projects, e.g., in collaboration with medicine, industry or other sciences is a plus.

We offer :

  • World-class research environment;
  • Numerous collaboration opportunities with researchers from EPFL and external partners from industry and academia;
  • Excellent working and living conditions.

Start date :
The starting date is flexible, but an earlier start date is preferred.

Term of employment :
Fixed-term (CDD)

Work rate :
100%

Duration :
Fixed-term contract (CDD), 1 year renewable, max. 2 years in total

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.