Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise
-
techniques to combine data from multiple sources, integrating both visual and textual information to deepen our understanding of patient health. This studentship offers a unique opportunity to contribute
-
MSc/PhD Position at the Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
research project focused on exploring the immunometabolic pathways of macrophages and microglia in multiple sclerosis (MS). The Kaushik Lab is committed to fostering an inclusive and equitable research
-
university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
-
, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
-
. Consider a self-adaptive IoT network such as a smart home that autonomously manages energy consumption while balancing multiple, often conflicting requirements, such as comfort, cost efficiency, and
-
knowledge of the English language high problem-solving ability, motivation/interest in scientific research, willingness to learn and work in a group applications showing previous experience with numerical
-
forcing wind turbines offline or heatwaves and droughts reducing cooling water availability for thermal power generation. Of particular concern are compound energy droughts, where multiple stressors occur
-
unpublished works). A publication list (if applicable). Include a brief description of your contributions if you have publications with multiple authors. A manuscript (article) derived from your master’s thesis
-
, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical