Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
. e.g. logistic regression, SVM, neural network models Basic experience with networking and client/server applications System level design- object oriented programming Basic knowledge of hardware- digital
-
responsibilities. Experience Essential: E1 Experience of analysing human body movement from sensor data (eg RGB videos and/or MOCAP data) using Deep Neural Networks (such as Graph Convolutional Networks). E2
-
Infrastructure? No Offer Description Walton Institute is looking to hire a Marie Skłodowska-Curie Action (MSCA) – Doctoral Candidate (DC) under project BRAINET – Networked Distributed Neural Interfaces
-
to the following: SDSC6012 – Time Series and Recurrent Neural Networks SDSC6016 – Predictive Analytics and Financial Applications SDSC8013 – Statistical Methods for Categorical Data Analysis (For detailed course
-
or equivalent Skills/Qualifications Technical Skills: MATLAB programming. PCB design. Specific Requirements Knowledge: Neural networks / Deep learning. Acoustics. Vibrations. AI: Artificial Intelligence
-
of research this position is engaged in: The Bowen lab leverages wide-scale neural recordings, predictive modeling, and continuous glucose monitoring with the goal of building foundational integrated (“multi
-
in Gait Training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(11), https://doi.org/10.1109/TNSRE.2016.2551642 * Friston, FitzGerald, Rigoli, Schwartenbeck & Pezzulo (2017
-
have demonstrated expertise in Natural Language Processing (NLP) and teaching. They should have the ability to teach both classical statistical methods and modern “black-box” approaches, including neural
-
interdisciplinary areas. Research fields of particular interest include, but not limited to: biomedical science and engineering veterinary science computer science and data science neuroscience and neural
-
, ensemble Kalman filters, and physics-informed neural networks (PINNs) enforce conservation laws while fitting observations. The key is to apply the vast amount of physical insights developed in turbulence