Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- France
- Norway
- Belgium
- Netherlands
- Denmark
- Singapore
- Spain
- Portugal
- Hong Kong
- Poland
- Australia
- Switzerland
- Austria
- China
- Canada
- Luxembourg
- United Arab Emirates
- Italy
- Ireland
- Czech
- Finland
- Cyprus
- Latvia
- Morocco
- Estonia
- India
- Andorra
- Bulgaria
- Lithuania
- New Zealand
- South Africa
- Brazil
- Romania
- Saudi Arabia
- Slovenia
- Worldwide
- Armenia
- Israel
- Japan
- Barbados
- Croatia
- Europe
- Greece
- Iceland
- Malta
- Slovakia
- Taiwan
- Vietnam
- 42 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Biology
- Engineering
- Economics
- Science
- Mathematics
- Business
- Chemistry
- Social Sciences
- Humanities
- Materials Science
- Arts and Literature
- Psychology
- Education
- Linguistics
- Electrical Engineering
- Environment
- Earth Sciences
- Physics
- Law
- Design
- Sports and Recreation
- Philosophy
- Statistics
- 15 more »
- « less
-
and Geophysics. Candidates should have a PhD in geology, geophysics or related field by the time of this appointment, be within 5 years of their PhD and have not held a permanent or tenured faculty
-
students who are prepared for a lifetime of learning and rewarding work. Candidates should hold a PhD or master’s degree in electrical and computer engineering or related fields and should be comfortable
-
PhD position in Human-Computer Interaction / Human-Centred Artificial Intelligence Help shape the future of work. This PhD project investigates how collaborative AI agents can support communication
-
% of the fellowship time to personal research. This is a one-year fellowship appointment, with the possibility of renewal for two additional years. Applicants must have fulfilled all the requirements for the PhD by
-
accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
-
Description REALISE - Bridging Igneous Petrology and Machine Learning for Science and Society About the REALISE Doctoral Network REALISE will train 15 Doctoral Candidates at the interface of igneous petrology
-
). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
-
duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong programming skills. Strong background in machine
-
research profile to further integrating wet-lab techniques (such as single-cell sequencing, -omics) with advanced data analysis, for example through bioinformatics, machine learning, or AI. Themes such as
-
programs targeting neurobiological disorders. Required Certification, Licensure/Other Credentials Preferred Qualifications Research experience in using in vivo neuroimaging and machine learning techniques