We are looking for an experienced web developer!
Intelen is looking to hire an experienced web developer to join its growing team in developing powerful energy efficiency and sustainability platforms.
The ideal candidate should be a strong team player, work well under tight deadlines and actively contribute in creating lean, efficient products that transform data into meaningful information for our clients. S/he also.
Why BiG? 3 innovation points of Intelen’s behavioral-based learning platform
Intelen BiG is a behavioral based learning system that raises eco-awareness and promotes behavioral change to an organization’s building occupants through gamification principles. The objective is dual: By using BiG, an organization is able to reduce its overall energy consumption and thus save money. At the same time, it will be able to raise the environmental awareness levels of the premises’ occupants in an engaging and fun way.
Two ways to save (on) gas you probably don’t know about!
With rising gas prices and rising environmental concerns, gas efficiency is on everyone’s mind these days, with automobile industries designing engines that give more and more mileage per gallon and consumers looking for ways to use those engines in the best, more economical way possible.
Are the usual energy efficiency solutions coming to their natural end?
As smart meters and their applications are becoming more and more commonplace among residential, commercial and industrial consumers alike, the question of how much more efficiency they can secure for building owners and businesses becomes all the more pressing: do retrofits, upgrades and the obvious energy management tweaks mark an efficiency “ceiling”, or is there another way to lower consumption and lower energy bills?
The less the merrier: dimensionality reduction and knowledge discovery
An increasing number of contemporary applications produce massive volumes of very high dimensional data. In scientific databases, for example, it is common to encounter large sets of observations, represented by hundreds or even thousands of variables. Typical cases include astronomical, energy and network applications. In order to extract knowledge from these datasets, we need to access the underlying, hidden information.