Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Harvard University
- National University of Singapore
- Simons Foundation;
- UNIVERSITY OF SURREY
- Aarhus University
- CRANFIELD UNIVERSITY
- Center for Devices and Radiological Health (CDRH)
- City of Hope
- Dana-Farber Cancer Institute (DFCI)
- Georgia Southern University
- Hong Kong Polytechnic University
- Imperial College London
- NTNU Norwegian University of Science and Technology
- Nanyang Technological University
- Oden Institute for Computational Engineering and Sciences
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- UCL;
- University of California
- University of Maryland, Baltimore
- University of Oslo
- University of Texas Rio Grande Valley
- University of Waterloo
- Zintellect
- 13 more »
- « less
-
Field
-
a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
-
experience with probabilistic or computational modelling. Experience with language model evaluation, cognitive modelling, reinforcement learning, goal-directed behaviour, learning theory or large-scale GPU
-
Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 3 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine
-
will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
-
data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web-tool development Present
-
: The R2L Lab explores how language understanding improves machine learning efficiency and generalization. We are a leader in agentic benchmarks and evaluation; our platforms serve as primary evaluation
-
: Probabilistic generative models (VLMs, diffusion, flow models) Reinforcement learning & Markov decision processes Causal inference & counterfactual reasoning Mechanistic & physics-informed modeling Agentic AI
-
try again. UiO/Anders Lien 1st October 2026 Languages English English English Postdoctoral Research Fellow in Quantum Machine Learning for Multimode Mechanics Apply for this job See advertisement About