The successful candidates will join the Data Mining & Machine Learning Group and contribute to a new research project, ROCSAFE (see below) funded by the European Union's Horizon 2020 Programme. The research is likely to involve one of: (1) advances in temporal Bayesian reasoning for decision support; (2) routing of autonomous vehicles for optimal collection of multi-resolution image and sensor data; (3) context-aware decision support driven by sensor data analytics.
As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this course, you'll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously.
Britain's manufacturing sector has just posted its strongest growth in over two years. Latest figures show that export orders have increased at their fastest rate since January 2014 and factories have also taken on more workers, with employment rising for the second consecutive month. However, the export benefits of a weakened pound will not last forever, and so, as the manufacturing sector continues to evolve, this year's FT Future of Manufacturing Summit looked at how big data analytics, advanced robotics, the Internet of Things (IoT) and additive manufacturing are shaping the economics of production and distribution within the sector. With the opportunities big data brings referred to as the Fourth Industrial Revolution in manufacturing, the estimated £57bn boost to the industry over the next five years will be driven by gains in efficiency through the use of big data analytics. The winners will be those who can adapt, embrace technologies and respond to new demands.
Berkeley Lab researchers Vahe Tshitoyan, Anubhav Jain, Leigh Weston, and John Dagdelen used machine learning to analyze 3.3 million abstracts from materials science papers. Sure, computers can be used to play grandmaster-level chess, but can they make scientific discoveries? Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.