Sort by
Refine Your Search
-
Country
-
Employer
- Oak Ridge National Laboratory
- Indiana University
- SUNY University at Buffalo
- Boston University
- Brookhaven National Laboratory
- Cornell University
- Duke University
- EPFL
- Harvard University
- KU LEUVEN
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Sandia National Laboratories
- Texas A&m Engineering
- University of Idaho
- 4 more »
- « less
-
Field
-
14 Jul 2026 Job Information Organisation/Company KU LEUVEN Research Field Computer science » Modelling tools Computer science » Programming Computer science » Systems design Technology » Computer
-
Understanding human motivation requires methods that go beyond questionnaires and simplified computer-based tasks. This project aims to develop more naturalistic, yet highly controlled, behavioral assays in which
-
. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
-
, optimization, and characterization integrating imaging, experimental metadata, and diffraction outcomes. Design and deploy computer vision methods to detect and track crystal growth. Develop closed-loop
-
the theory of brain-inspired algorithms and apply them to complex, real-world problems. The successful candidate will join an interdisciplinary team of computer scientists, mathematicians, engineers, and
-
models (DDM, sequential sampling, Bayesian models). Experience with computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection
-
that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
-
areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network