Cambridge Residency Programme: Information Theory and Signal Processing Scientist
Found in: Talent UK C2 - 1 week ago
Overview
We are seeking a researcher with expertise in information theory and signal processing to work on revolutionary new data storage technology.
This is a two-year fixed-term post-doctoral position and is part of our Cambridge Residency programme within the Cloud Systems Futures research group. Project Silica is an exciting new data storage technology. It works by storing data in glass using lasers, reading the data using optical imaging, and decoding it using advanced signal processing and machine learning.
We need a signal processing and information theory researcher to help us identify and remove sources of noise, devise improved coding approaches to optimise channel capacity and help improve the writing and reading processes, and work closely with machine learning experts to maximise total system efficiency.
Our team is highly multi-disciplinary team and innovates in all fields from the materials science to the cloud architecture. The successful candidate will need to demonstrate an aptitude to work with experts many of these fields and drive improvements for the whole storage system.
Qualifications
Required qualifications
PhD in Communications Engineering, Electronic Engineering, Computer Science, Mathematics, or a related field, or equivalent training and experience in research. Strong background in theoretical and computational methods of information theory and signal processing. Experience in developing and applying signal processing algorithms. Strong software development skills (e.g. in Python). Excellent written and verbal communication skills. Ability to work independently and as part of a cross-disciplinary team.Preferred qualifications
Knowledge of advanced digital signal processing. Knowledge of data compression and error correction techniques. Deep understanding of information theory and signal processing from first principles. Introductory knowledge of machine learning terminology and concepts.
Responsibilities
Theoretical and practical analysis of information channels within data storage systems. Devise, develop and apply improvements in channel coding, or related techniques, to research-grade prototype storage systems. Collaborate with machine learning experts to enhance statistical and information theoretic foundations of our machine learning models. Actively work with other disciplines to drive whole system improvements. Propose hypotheses, conceive experiments, create software, and share results. Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.Industry leading healthcareEducational resourcesDiscounts on products and servicesSavings and investmentsMaternity and paternity leaveGenerous time awayGiving programsOpportunities to network and connect-
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