253 algorithms-"EPFL" "INSAIT The Institute for Computer Science" Fellowship positions
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for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries of performance, speed, and precision
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instructing or supervision of labs and tutorials. Prior teaching experience is preferred for candidates. It would be good if the candidate can also explore areas such as multimodal algorithms/techniques
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objectives of the project are to (i) deploy network analysis methods to genomic data (50%), and (ii) develop such algorithms including community detection algorithms (50%). PhD in computer science, engineering
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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, and may utilise iterative algorithms, machine learning and high-performance computing. Through the Monash Centre for Electron Microscopy, opportunities exist to acquire large experimental datasets using
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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systems on software defined radio (SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide
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algorithms, and innovative mechanical design to replicate the remarkable flight capabilities observed in nature. The successful candidate will be part of a dynamic, multidisciplinary team of experts in
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to the development of innovative and sustainable low carbon plastic waste management and recycling solutions. In this project the post holder will develop novel algorithms and methods for analysis of plastic data
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees