Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- NEW YORK UNIVERSITY ABU DHABI
- Carnegie Mellon University
- Chalmers University of Technology
- Cornell University
- Eindhoven University of Technology (TU/e)
- Aarhus University
- EPFL
- Harvard University
- KTH Royal Institute of Technology
- Maastricht University (UM)
- Oak Ridge National Laboratory
- Pennsylvania State University
- Brookhaven National Laboratory
- CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences
- ETH Zürich
- FAPESP - São Paulo Research Foundation
- IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences)
- MOHAMED BIN ZAYED UNIVERSITY OF ARTIFICIAL INTELLIGENCE
- Massachusetts Institute of Technology
- Mohamed bin Zayed University of Artificial Intelligence
- Nencki Institute of Experimental Biology
- New York University
- Norwegian University of Life Sciences (NMBU)
- Princeton University
- Technical University of Munich
- The University of Arizona
- University College Cork
- University of Arkansas
- University of Lund
- University of Oxford
- University of Oxford;
- Université de Caen Normandie
- VIB
- 24 more »
- « less
-
Field
-
depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
-
). This interdisciplinary project investigates how AI — specifically large language model (LLM)-based agents — can act as adaptive social agents to support students' collaborative learning in Challenge-Based Learning (CBL
-
data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
-
processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude for team work and excellent communication skills in spoken and written
-
your academic career? Your interests lie in the field of machine learning techniques, particularly artificial neural networks, and deep learning? And you would like to continue your research
-
measurement techniques and PIV. Familiarity with optics, lasers, image processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude
-
what you bring. Do you recognize yourself in this? You have completed a PhD in data science and/or artificial intelligence. You have gained knowledge of various machine learning techniques, particularly
-
, established track record of publications in computational microscopy, computer vision, or parallel machine learning Adaptability: A demonstrated, strong willingness to learn and bridge the gap
-
foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
-
science, or related computational approaches. Candidates must have experience developing or implementing machine learning methods for large-scale data analysis, predictive modeling, or biological discovery