Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nanyang Technological University
- National University of Singapore
- Zintellect
- University of Oslo
- University of Bergen
- University of Maryland, Baltimore
- University of Texas at Austin
- Faculdade de Ciências da Universidade de Lisboa
- Harvard University
- Hong Kong Polytechnic University
- Johns Hopkins University
- Marquette University
- Oslo University Hospital
- SUNY University at Buffalo
- The Francis Crick Institute;
- UiT The Arctic University of Norway
- Universidade de Coimbra
- Universidade do Minho
- University of Sussex;
- 9 more »
- « less
-
Field
-
project - “Oracle complexity bounds of first-order methods for nonsmooth optimization with nonconvex function constraints”. They will be required to conduct theoretical analysis, algorithm implementation
-
The successful candidate will work with Asst. Prof. Shen Shuting on combinatorial inference under a project on "Post-learning inference for near-optimal discrete structures". The main
-
This postdoc will work under the supervision of Dr. Guanyi Wang in ISEM at NUS to explore cutting-edge algorithms, derive rigorous theoretical guarantees, and implement numerical simulations
-
programming, distributionally robust optimization, optimal transport, or reinforcement learning is highly desirable; • Programming skills in Python are desirable, especially experience with numerical
-
, and Large Language Modelling (LLM). Conduct extensive numerical experiments to validate and evaluate the performance of the proposed models. Present research results as academic papers and reports
-
with demonstrated ability to implement and optimize AI/ML models for biomedical datasets. Preferred Knowledge, Skills and Abilities Mathematical Modeling: Strong foundation in numerical modeling, graph
-
-conversion, and quasi-phase matching. 4. Technical proficiency in numerical modeling and analysis of optical waveguides, photonic structures, and nonlinear optical processes; design and optimization of van der
-
for existing commercial products. The work will involve numerical investigation of two-phase flows over louvre panels and experimental verification as well as optimal design by using neural network techniques
-
explore relations between energy and non-classicality, both to provide new ways to characterize non-classical quantum states of light, and optimize processes to produce them. Context and goal of the project
-
matching, optimal transport or cell-cycle modelling. Key Responsibilities These include but are not limited to: Leading an independent research project in scientific machine learning and mechanistic