Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
these decisions? Required knowledge This project is open to candidates from diverse academic backgrounds, including computer science, data science, learning sciences, or educational technology. While prior
-
Industry Innovation Program Scholarship The Embedded Co-Op Scholarship funded by an Industry Partner via the corresponding Faculty be introduced to allow industry and students to directly interact
-
infrastructure. Its mission is to accelerate discovery in addressing some of humanity's most pressing challenges, from combating disease to advancing environmental science. By uniting high-performance computing
-
operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able
-
Cooperative Education Program* administered out of the Faculty of Engineering. * Co-op Program student pre-requisites: Students must be enrolled in a single or double engineering degree Students are in
-
of Women’s Health program outcomes through a range of laboratory and technical activities, including sample preparation, cell and tissue culture and performing assays such as qRT-PCR, immunoblotting
-
healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series
-
the Monash Engineering program. Am I eligible? You must be one of the following: An International student You must meet the following criteria: A commencing student enrolled or intending to enrol in
-
We have several PhD and Research Assistant (RA) opportunities available in areas such as Multimodal Large Language Models (MLLM) for human understanding, MLLM safety, and Generative AI. If you have published in top-tier conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, etc.), you will have a strong...
-
privacy-enhancing techniques such as secure multi-party computation, homomorphic encryption, differential privacy, and trusted execution to design algorithms and protocols to secure ML models within