11 computer-programmer-"Multiple"-"U"-"U.S"-"Embry-Riddle-Aeronautical-University" positions in France
Sort by
Refine Your Search
-
Category
-
Program
-
Employer
-
Field
-
-CHANGE is an interdisciplinary research program aimed at enhancing neurological health resilience in the face of global change through a One Health approach. By collaborating with Luxembourgish and
-
on FNR’s prestigious PEARL program, and has the option for an affiliated professorship. Both LIH and DFKI pledge their full commitment to ensuring that this new position becomes a cornerstone in the
-
areas Personalized learning programme to foster our staff’s soft and technical skills Multicultural and international work environment with more than 50 nationalities represented in our workforce Diverse
-
curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills · Multicultural and international work environment with
-
; Participate in lab meetings and scientific discussions. Key Skills, Experience & Qualifications Education & Experience: Enrolled in a Master’s programme (M2) in biology, immunology, oncology, or a closely
-
program in aging research and/or age-related pathologies, including cancer. Collaborate with world-class researchers in a multidisciplinary environment. Contribute to groundbreaking research aimed
-
encouraging curiosity, innovation and entrepreneurship in all areas · Personalized learning programme to foster our staff’s soft and technical skills · Multicultural and international work
-
Virtual laboratory to predict the ability of a fluctuating biomass to satisfy a material use-VARIOUS
the Master’s 2 and the Graduate Programme “Materials Science” option “Innovative materials, advanced technologies and modelling”. These lessons are necessary to study the behaviour of biobased products
-
signatures. We hypothesize that this unique program may represent an ancient state of some skeletal muscles fibres and be required for unique mechanical functions. This project aims to address the following
-
experimental data and is testable across multiple unlearning scenarios. For this we plan to apply for the first time Spiking Neural Networks (SNNs) to the modeling of unlearning. SNNs have recently shown