Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Sweden
- United Arab Emirates
- Denmark
- United Kingdom
- Netherlands
- Germany
- Morocco
- Finland
- France
- Norway
- Poland
- Belgium
- Switzerland
- Hong Kong
- Ireland
- Luxembourg
- Spain
- Australia
- China
- Taiwan
- Austria
- Japan
- Portugal
- Slovenia
- Brazil
- Canada
- Cyprus
- Greece
- Israel
- Singapore
- South Africa
- 22 more »
- « less
-
Field
-
motivated post-doctoral associate with a strong background in control systems and machine learning to join the research team of Prof. M. Umar B. Niazi. The position focuses on the development of digital twins
-
: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
-
mathematics Appl Deadline: (posted 2025/12/11, listed until 2026/01/16) Position Description: Apply Position Description Postdoc in Algebra-Geometric Foundations of Deep Learning or Computer Vision KTH Royal
-
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning for batteries, with
-
: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
-
Responsibilities will vary depending on the Fellow’s background, but may include: Developing machine learning, optimization, or simulation models to improve clinical operations and resource allocation Advancing
-
(postdoc) Limited until: permanent Reference no.: 4984 Among the many reasons to research and teach at the University of Vienna there is one in particular, which has convinced around 7,500 academic staff
-
or recent Ph.D. in applied/computational mathematics, computational science, chemical engineering, or related disciplines. Ideal candidates will have strong past experience in scientific machine learning
-
frameworks for quantum machine learning, including conformal quantum prediction and uncertainty quantification in quantum models; Theoretical and algorithmic advances rooted in statistical learning theory
-
will work closely with Drs. Audrey Hendricks and Ryan Layer as well as grad students, postdocs, and other collaborators to develop and implement statistical and machine learning methods, software