Archive for Intel

fellowship openings at the Alan Turing Institute

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , on November 17, 2015 by xi'an

[Verbatim from the  Alan Turing Institute webpage]Alan Turing Fellowships

This is a unique opportunity for early career researchers to join The Alan Turing Institute. The Alan Turing Institute is the UK’s new national data science institute, established to bring together world-leading expertise to provide leadership in the emerging field of data science. The Institute has been founded by the universities of Cambridge, Edinburgh, Oxford, UCL and Warwick and EPSRC.

Fellowships are available for 3 years with the potential for an additional 2 years of support following interim review. Fellows will pursue research based at the Institute hub in the British Library, London. Fellowships will be awarded to individual candidates and fellows will be employed by a joint venture partner university (Cambridge, Edinburgh, Oxford, UCL or Warwick).

Key requirements: Successful candidates are expected to have i) a PhD in a data science (or adjacent) subject (or to have submitted their doctorate before taking up the post), ii) an excellent publication record and/or demonstrated excellent research potential such as via preprints, iii) a novel and challenging research agenda that will advance the strategic objectives of the Institute, and iv) leadership potential. Fellowships are open to all qualified applicants regardless of background.

Alan Turing Fellowship applications can be made in all data science research areas. The Institute’s research roadmap is available here. In addition to this open call, there are two specific fellowship programmes:

Fellowships addressing data-centric engineering

The Lloyd’s Register Foundation (LRF) / Alan Turing Institute programme to support data-centric engineering is a 5-year, £10M global programme, delivered through a partnership between LRF and the Alan Turing Institute. This programme will secure high technical standards (for example the next-generation algorithms and analytics) to enhance the safety of life and property around the major infrastructure upon which modern society relies. For further information on data-centric engineering, see LRF’s Foresight Review of Big Data. Applications for Fellowships under this call, which address the aims of the LRF/Turing programme, may also be considered for funding under the data-centric engineering programme. Fellowships awarded under this programme may vary from the conditions given above; for more details contact fellowship@turing.ac.uk.

Fellowships addressing data analytics and high-performance computing

Intel and the Alan Turing Institute will be supporting additional Fellowships in data analytics and high-performance computing. Applications for Fellowships under this call may also be considered for funding under the joint Intel-Alan Turing Institute programme. Fellowships awarded under this joint programme may vary from the conditions given above; for more details contact fellowship@turing.ac.uk.

Download full information on the Turing fellowships here

Diversity and equality are promoted in all aspects of the recruitment and career management of our researchers. In keeping with the principles of the Institute, we especially encourage applications from female researchers

Truly random?!

Posted in Books, R, Statistics, University life with tags , , , , , , , on September 7, 2010 by xi'an

Having purchased the September edition of La Recherche because of its (disappointing!) coverage on black matter, I came by a short coverage on an Intel circuit producing “truly random” numbers… I already discussed this issue in an earlier post, namely that there is no reason physical generators are “more” random than congruential pseudo-random generators, but this short paper repeats the same misunderstanding on the role of “random” generators. The paper mentions dangers of pseudo-random generators for cryptography (but this is only when you know the deterministic function and the sequence of seeds used so far), while it misses the essential aspect of valid generators, namely that their distribution is exactly known (e.g., uniform) and, in the case of parallel generations, which seems to be the case for this circuit, that the generators are completely independent. La Recherche mentions that the entropy of the generator is really high, but this is more worrying than reassuring, as the Intel engineers do not have a more elaborate way to prove uniformity than a Monte Carlo experiment…

There is actually a deeper entry found on Technology Review. (Which may have been the source for the paper in the technology tribune of La Recherche.) The article mentions that the generator satisfied all benchmarks of “randomness” maintained by NIST. Those statistical tests sound much more reassuring than the entropy check mentioned by La Recherche, as they essentially reproduce Marsaglia’s DieHard benchmark… I remain rather skeptical about physical devices, as compared with mathematical functions, because of (a) non-reproducibility which is a negative feature despite what the paper says and of (b) instability of the device, which means that proven uniformity at time t does not induce uniformity at time t+1. Nonetheless, if the gains in execution are gigantic, it may be worth the approximation for most applications. But please stop using “true” in conjunction with randomness!!!