Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Sweden
- Germany
- Norway
- Denmark
- France
- Netherlands
- Poland
- Austria
- Spain
- Belgium
- Luxembourg
- United Kingdom
- Finland
- Singapore
- Switzerland
- Canada
- China
- Italy
- Portugal
- Saudi Arabia
- Ireland
- Romania
- Bulgaria
- Cyprus
- Slovenia
- Worldwide
- Andorra
- Brazil
- Hong Kong
- Japan
- Latvia
- Taiwan
- United Arab Emirates
- 24 more »
- « less
-
Program
-
Field
-
and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
-
combined with human support. To learn more please visit https://www.kcl.ac.uk/research/embrace About the role The Research Fellow in Digital Health & Data Sciences is focused on the development and
-
, machine learning, etc. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and microwave engineers, computer scientists, software
-
faculty, postdocs, and students across physics, biology, engineering, and other STEM fields. Fellows will be provided an annual salary of $75,000, full benefits, and a research account of $4,000 per year
-
members have been working on statistics learning, granular computing and knowledge discovery, machine learning, deep learning, and specifically interpretable artificial intelligence. Many innovative
-
of working with motion capture, eye tracking, machine learning, or other advanced behavioral analyses or related research experiences. A consistently excellent academic track record is required, including
-
. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
-
-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
-
for seasonal prediction using hybrid physics-machine learning models in R&D item Research on Seasonal Meteorological and Oceanographic Forecast Simulator under Development of Integrated Simulation Platform
-
experimental approaches, including machine learning, genomic assays, and live imaging of subcellular dynamics coupled to CRIPSR-based genome engineering. Much of the experimental work is carried out in live