Postdoctoral research assistant in detailed and deep image understanding Starting preferably before August 31st, 2014 or as soon as possible thereafter
We are seeking a Postdoctoral research assistant to join the Visual Geometry Group (VGG) in the Oxford Engineering Department. The post is funded by the UK Engineering and Physical Sciences Research Council (EPSRC), and is fixed-term up to 12 months.
Applications are invited for a two year postdoc position in one of the following areas:
- Theory of Bayesian inference under misspecification
- On-Line Sequential Prediction
- Pseudo-Bayesian methods, in particular PAC-Bayesian confidence bounds
- notions of 'easy learning problems' and their interconnections (Tsybakov-Mammen margin condition, mixability, exp-concavity etc.)
- information-theoretic vs. statistical notions of risk
- foundational issues in Bayesian inference such as the Robins-Riitov paradox and connections between p-values and Bayes factors
Cortexica, an award-winning spinout company from Imperial College London, is actively recruiting researchers to work on cutting-edge projects in machine learning and computer vision. As one of the world’s leading developers of image recognition and visual search, the company has created a bio-inspired visual processing platform and is focused on exploiting opportunities in the fashion vertical. The company’s aim is to bring world class image processing and recognition to a breadth of commercial applications and international partners through its hosted API platform.
Multiview Learning for Sequence Extraction Tasks
With the increase of electronically available textual information in different views, new needs for Information Access systems are arising. Many new tasks lie between the classic frameworks of Information Retrieval (IR) and Information Extraction (IE). Machine Learning (ML) is playing a central role in the development of these fields but has been used for the most part for the improvement of existing models.
International Workshop on Technical Computing for Machine Learning and Mathematical Engineering
8 - 12 September, 2014 - Leuven, Belgium
Workshop homepage: http://www.esat.kuleuven.be/stadius/tcmm2014/
Postdoc position on conversation summarization
(Full time, one year - Closing date for applications 2014-07-01)
We are looking for an outstanding research scientist to join the
"SENSEI" european project (http://www.sensei-conversation.eu/). You
will contribute to conversation analysis summarization research to
allow the exploitation of large quantity of comments in social
media and spoken conversations.
You will contribute to the design and development of speech and text
summarization technologies for conversational data such as social
The LIG (Grenoble, France) and AAI (Sydney, Australia) offer a
fully-funded 3 year PhD position on "Scaling Latent Topic/Class Models
to Big Data Collections and Streams".
Numerous pieces of content are currently exchanged in social media,
making them an important source of information. For example, people
share, per month, 30 billion pieces of content on Facebook and over 5
billion tweets (see for example the site mashable.com). This importance
is also reflected in the fact that, when searching for information
Have you recently completed or expect very soon an MSc or equivalent degree in computer science, artificial intelligence, computational linguistics, engineering or a closely related area? Are you interested in carrying out research on NLP during the next years? Are you excited to spend a part of your life in a pleasant city in the heart of the Italian Alps?
WE ARE LOOKING FOR YOU!!!
**Perception and Understanding of Urban Driving Scenes**
One-year position starting on September 1st, 2014 - CNRS / University of Technology of Compiegne, France.
Depending on his/her abilities and interests, the post-doctoral fellow will be involved in at least one of the following actions:
1 - Real-time perception for intelligent road vehicles in dynamic urban environments by fusing multimodal sensing with prior knowledge (e.g. geo-referenced maps). This action includes SLAM with moving object detection and tracking, etc.
About a week ago we opened the Higgs boson machine learning challenge (HiggsML) to promote collaboration between high-energy physicists and computer scientists. For the first time, the ATLAS experiment at CERN made public some of the simulated data used by physicists to optimize the analysis of the Higgs boson. The Challenge is organized by a small group of ATLAS physicists and computer scientists from LAL (Université Paris Sud and CNRS/IN2P3), LRI (Université Paris Sud and CNRS), INRIA, Royal Holloway University of London, and ChaLearn. It is hosted by Kaggle at