Sort by
Refine Your Search
-
Listed
-
Category
-
Country
- United States
- Germany
- United Kingdom
- Netherlands
- Portugal
- Sweden
- Norway
- Belgium
- Denmark
- Switzerland
- Spain
- France
- Austria
- Australia
- United Arab Emirates
- Ireland
- Czech
- Luxembourg
- Canada
- Poland
- Estonia
- New Zealand
- Hong Kong
- India
- Italy
- Romania
- Armenia
- Brazil
- Finland
- Indonesia
- Israel
- Latvia
- Singapore
- Slovenia
- Vietnam
- 25 more »
- « less
-
Program
-
Field
- Computer Science
- Medical Sciences
- Engineering
- Science
- Economics
- Biology
- Mathematics
- Psychology
- Arts and Literature
- Education
- Social Sciences
- Business
- Chemistry
- Environment
- Humanities
- Linguistics
- Materials Science
- Earth Sciences
- Electrical Engineering
- Law
- Design
- Philosophy
- Physics
- Sports and Recreation
- 14 more »
- « less
-
on immunotherapy response. Where to apply Website https://ibb.edu.pl/en/phd-studies/rekrutacja/ Requirements Research FieldBiological sciencesEducation LevelMaster Degree or equivalent Skills/Qualifications Holding
-
point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
-
for the PhD admission is available at TalTech´s web-page: https://taltech.ee/en/phd-admission The following application documents should be sent to tarmo.soomere@taltech.ee CV Motivation letter Degree
-
urban design with microclimate simulations and measurements, GIS and Digital Twin technologies, and machine learning. The work will be part of a Horizon pilot project aimed at realizing a scenario-based
-
physical). Solid background in programming and experience with machine learning. Knowledge of participatory design and co-creation methodologies. Ability to learn independently and passion for research
-
at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
-
promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research
-
of faculty supervisor, develop novel techniques incorporating machine learning in particle physics event generators. Contribute to the development of machine learning driven techniques in the Pythia 8 event
-
materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
-
. Machine learning will assist in artifact correction, segmentation, and material classification. By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high