Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- Carnegie Mellon University
- University of Luxembourg
- University of Texas at El Paso
- University of Massachusetts Medical School
- AALTO UNIVERSITY
- Auburn University
- CNRS UMR6614 CORIA
- Chalmers University of Technology
- Constructor University Bremen gGmbH
- Duke University
- IMEC
- Imperial College London;
- Max Planck Institute for Intelligent Systems, Tübingen, Tübingen
- Michigan State University
- NTNU Norwegian University of Science and Technology
- Purdue University
- Science me Up
- Tampere University
- Technical University of Munich
- The University of Chicago
- University of Lund
- 12 more »
- « less
-
Field
-
cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years
-
Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models
-
in at least one major programming language, such as Python, is expected Familiarity with deep learning frameworks and modern NLP toolkits is an advantage Motivation to publish research results in
-
etc; Candidates with multidisciplinary backgrounds are welcome. · Strong skills in computational and data analytical methodology development and implementation; experience in machine learning and deep
-
. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
-
Nanoengineering department. In this position you will have a chance to take a deep dive into quantum computing and make a real impact by high-quality research. Join us in shaping the future! Your role and goals
-
Science. Commitment to undergraduate and graduate education. Demonstrated expertise in machine learning/deep learning and software development (Python; PyTorch/TensorFlow). Peer-reviewed publications and strong
-
, unsupervised, and deep learning. Proficiency in appropriate model selection; simpler or complex models, depending on the data. Ability to work effectively within diverse, multidisciplinary teams of faculty
-
measurement, four-point probe for resistivity, deep-level transient spectroscopy, and a semiconductor parameter analyzer. Job Description: The Department of Electrical and Computer Engineering (ECE
-
, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in international conferences and related