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CITY, UNIVERSITY OF LONDON

School of Mathematics, Computer Science & EngineeringComputer Science

Professor in Computer Vision and Machine Learning

SALARY: Competitive

Founded in 1894, City, University of London is a global university committed to academic excellence with a focus on business and the professions and an enviable central London location.

City attracts around 20,000 students (over 40% at postgraduate level) from more than 150 countries and staff from over 75 countries.

In the last decade City has almost tripled the proportion of its total academic staff producing world-leading or internationally excellent research.

Led by President, Professor Sir Paul Curran, City has made significant investments in its academic staff, its estate and its infrastructure and continues to work towards realising its vision of being a leading global university.

The School of Mathematics, Computer Science & Engineering is a multi-disciplinary centre of research and education located in the heart of Londons vibrant design community. It is proud of its research advances and of educating thousands of undergraduates and postgraduates in STEM subjects.

The Department of Computer Science has been at the leading edge of computer science in the UK for six decades. It awarded some of the countrys first Computer Science degrees and laid the groundwork for the foundation of the British Computer Society. Today, it is a vibrant, modern department comprising approximately 50 academic staff and 60 research staff and PhD students.

The School is seeking to appoint a Professor in Computer Vision and Machine Learning who will join the Research Centre for Adaptive Computing Systems and Machine Learning (ACS-ML) and collaborate closely with Tesco plc on research for the retail sector. The appointed candidate will lead and foster excellent research; contribute to the delivery of high quality undergraduate and postgraduate education in core Computer Science; and play a lead role in developing the partnership with Tesco and strengthening expertise in Computer Vision for the retail sector.

The successful candidate will have a PhD in Computer Science or an area related to machine learning, artificial intelligence or computer vision; an internationally recognised reputation in such an area; a track record of world-leading or internationally excellent research; and experience of delivering high quality education in core Computer Science. A track record of generating research income and of delivering consultancy or specialist services to external clients will also be required.

City offers a sector-leading salary, pension scheme and benefits including a comprehensive package of staff training and development.

The role is available immediately.

Closing date: Friday 11th December 2020

Interviews are scheduled for January 2021

For a confidential discussion, please contact Imogen Wilde on +44 (0)7864 652 633 or Elliott Rae on +44 (0)7584 078 534.

For further information, please visit http://www.andersonquigley.com/city-prof

Actively working to promote equal opportunity and diversityAcademic excellence for business and the professions

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Professor in Computer Vision and Machine Learning job with CITY, UNIVERSITY OF LONDON | 232985 - Times Higher Education (THE)

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Post Covid-19 Impact on Machine Learning in Communication Sales, Price, Revenue, Gross Margin and Market Share 2020 to 2026 Amazon, IBM, Microsoft,...

The real possibility of advancing intelligence through deep learning and other AI-driven technology applied to video is that, in the long term, were not going to be looking at the video until after something has happened. The goal of gathering this high level of intelligence through video has the potential to be automated to the point that security operators will not be required to make the decisions necessary for response. Instead, the intelligence-driven next steps will be automatically communicated to various stakeholders from on-site guards to local police/fire departments. Instead, when security leaders access the video that corresponds to an incident, it will be because they want to see the incident for themselves. And isnt the automation, the ability to streamline response, and the instantaneous response the goal of an overall, data-rich surveillance strategy? For almost any enterprise, the answer is yes.

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Benefits Of AI And Machine Learning | Expert Panel | Security News - SecurityInformed

Overcome the skills shortage and alert fatigue with advanced machine learning and automation technology.

Advanced threats, a shortage of security experts and the rise in work-from-home together form a catalyst for MSPs to enhance cybersecurity effectiveness for their customers. As MSPs seek ways to increase efficiency and do more with less, theyre turning to advanced analytical capabilities like machine learning, security analytics and automation. All of these have moved past their initial hype cycle and are now adopted and delivering enhanced ROI and outcomes in IT and cybersecurity.

The future of your business is Big Data and Machine Learningtied to the business opportunities and customer challenges before you.

Eric Schmidt, then CEO of GoogleCloudNext Conference in 2017

Machine learning and automation are more than popular buzzwords in the cybersecurity industry. These analytic capabilities make sense of large volumes of raw data to create context and find unknown attacks that speed up decision making. When combined with cybersecurity experts, they hold real promise for their ability to transform IT and security operations for organizations of all sizes. While not a magic potion that instantly perfects data security, these advanced tools offer MSPs a way to augment limited staff in the ongoing battle against cyber criminals.

The Value of Machine Learning and Automation in Cybersecurity

With digital transformation serving as a catalyst for larger volumes of data and technology, use cases for ML and automation in IT and security operations are growing. While not exhaustive, key use cases include:

Analyzing vast reams of data for suspicious activity: Its challenging to process billions of logs with an all-manual approach. Machine learning does the initial correlation work to process incoming log streams, reduce false positives and alert security operations center (SOC) analysts who perform a second level of triage and potential threat hunting.

Improving SOC efficiency and effectiveness: Machine learning and automation manage repetitive and potentially error-prone tasks that can overwhelm security teams. The result is higher job satisfaction and retention of hard-to-find cybersecurity professionals.

Increasing speed, accuracy and scale of threat detection: Automated incident response can launch a set of corrective actions, open a ticket for SOC triage and even block suspicious processes. Faster detection and remediation reduce the potential damage of attackers.

Detecting anomalous behavior by users and supply chain partners: Detect insider threats and advanced attacks with machine learning to understand and predict normal baseline system activity and identify exceptions that signal a cybersecurity risk. A SIEM (security information and event management) solution provides user and entity behavior analysis (UEBA) to detect insider threats, lateral movement and advanced attacks.

Through advancements and adoption of machine learning and security automation, MSPs are harnessing the vast reams of device and client data to foster better cyber decision making.

Cyber Criminals Also Embrace Advanced Tools

Defenders arent the only ones looking at emerging technologies. Global cybercrime damages are predicted to reach $6 trillion annually by 2021, according to the2019 Annual Cybercrime Report by Cybersecurity Ventures. Cybercriminals are upping their game to use the latest tools and technology to improve outcomes for their exploits. Hackers are using

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MSPs are Bolstering Security Programs with Machine Learning and Automation - Channel Futures

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value.

However, the challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment.

In response, new solutions have emerged to deliver key capabilities in areas including visualization, self-service, and real-time analytics. Along with the rise of DataOps, greater collaboration, and automation have been identified as key success factors.

DBTA recently hosted a special roundtable webinar featuring Alyssa Simpson Rochwerger, VP of AI and data, Appen; Doug Freud, SAP platform and technology global center of excellence, VP of data science; and Robert Stanley, senior director, special projects, Melissa Informatics, who discussed new technologies and strategies for expanding data science and machine learning capabilities.

According to a Gartner 2020 CIO survey, only 20% of AI projects deploy, Rochwerger said. The top challenges are skills of staff, understanding the benefits and uses of AI, and the data scope and quality.

She said businesses need to start out by clarifying a goal so they can then know where the data is coming from. Once organizations know where the data is coming from, they can find and fill in the gaps. Having a diverse team of humans can make it easier to sift and combine data.

According to Data2020: State of Big Data Study Regina Corso Consulting 2017, 86% of companies arent getting the most out of their data and they are limited by data complexity and sprawl, Freud explained.

SAP Data Intelligence can meet companies in the middle, Freud said. The platform boasts that its enterprise AI meets intelligent information management.

The platform features benefits that include:

Stanley took another approach by introducing the concept of data quality (DQ) fundamentals with AI. AI can be useful for DQ, particularly with unstructured or more complex data, bringing competitive advantage.

Using AI (MR and ML), more efficient methods for identification, extraction and normalization has been developed. AI on clean data enables pattern recognition, discovery and intelligent action.

Machine reasoning (MR) relies on knowledge captured and applied within ontologies using graph database technologies - most formally, using SDBs, he explained.

Machine reasoning can make sense out of incomplete or noisy data, making it possible to answer difficult questions. MR delivers highly confident decision-making by applying existing knowledge and ontology-enable logic to data, Stanley noted.

An archived on-demand replay of this webinar is available here.

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Going Deeper with Data Science and Machine Learning - Database Trends and Applications