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Come work with us Interested in working with researchers from different disciplines within the Harvard, MIT and Broad community and an unique opportunity to participate in world-class research to make an impact on human health? Come work with us! We are looking for a computational biologists to handle data from a wide variety of experimental methods, focusing on next-gen sequencing technologies. Keep Reading...  SCDE is live The Stem Cell Discovery Engine (SCDE) is an integrated platform that allows users to consistently describe, share and compare cancer and tissue stem cell data. It is made up of an online database of curated experiments coupled to a customized instance of the Galaxy analysis engine with tools for gene list manipulation and molecular profile comparisons. The SCDE currently contains more than 50 stem cell-related experiments. Each has been manually curated and encoded using the ISA-Tab standard to ensure the quality of the data and its annotation. Keep Reading...  The Center for Health Bioinformatics at the Harvard School of Public Health provides consults to researchers for the management, integration and contextual analysis of biological high-throughput data. We are a member of the Center for Stem Cell Bioinformatics, the Environmental Statistics and Bioinformatics Core at the Harvard NIEHS Center for Environmental Health and the Genetics & Bioinformatics Consulting group for Harvard Catalyst and work closely with our colleagues in the Department of Biostatistics and the Program in Quantitative Genomics to act as a single point of contact for computational biology,

NEIL (Never Ending Image Learner) is a computer program that runs 24 hours per day and 7 days per week to automatically extract visual knowledge from Internet data. It is an effort to build the world's largest visual knowledge base with minimum human labeling effort - one that would be useful to many computer vision and AI efforts. See current statistics about how much NEIL knows about our world!!

"*Der realraum zieht um* und nicht nur das: OLGA zieht mit uns um und wird Teil des realraum werden. Damit wird der realraum vom Treffpunkt für Computer- und Elektronik-Interessierte zum erwachsenen Hacker- und Makerspace für die Bereiche Computer, Molekularbiologie, Software, Pharmazie, Elektrotechnik, Biotechnologie und Telematik. Ein Treffpunkt für Alle die Spaß am Tüfteln und Technik haben. Ab circa Oktober findet Ihr uns hier, um's Eck von der Neuen Technik"

"I am a Neuroscientist working at the Centre for Integrative Neuroscience (CIN), University of Tübingen, Germany. In My Research I use a combination of 2-photon imaging, electrophysiology and computational modelling to unravel principles of synaptic and network computations in the vertebrate early visual system. Outside my regular work I am also co-founder of a not-for-profit organisation TReND in Africa, dedicated to foster Neuroscience Education and Research on the African continent. Moreover I am contributor to Open Labware, the design and building of open source laboratory equipment based on off-the-shelf electronics and simple mechanics as made possible by 3D printing"

The shrinking of the transistor has approached fundamental physical limits. Increasingly, transistor manufacturers grapple with subatomic effects, like the tendency for electrons to "leak" across material boundaries. The leaking electrons make it more difficult to know when a transistor is in an on or off state, the information that makes electronic computing possible. They have also led to excess heat, the bane of the fastest computer chips.

Princeton engineers have made a breakthrough in an 80-year-old quandary in quantum physics, paving the way for the development of new materials that could make electronic devices smaller and cars more energy efficient.

A new algorithm may give quantum computers a new, practical job: quickly solving monster linear equations. Such problems are at the heart of complex processes such as image and video processing, genetic analyses and even Internet traffic control.

"The Natural Language Processing Group at Stanford University is a team of faculty, research scientists, postdocs, programmers and students who work together on algorithms that allow computers to process and understand human languages. Our work ranges from basic research in computational linguistics to key applications in human language technology, and covers areas such as sentence understanding, machine translation, probabilistic parsing and tagging, biomedical information extraction, grammar induction, word sense disambiguation, automatic question answering, and text to 3D scene generation."

"Computational Network Toolkit Production-quality, Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training, Speech, Image, Text"

"Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation."

Science is an assault on ignorance, Its legacies are concepts, technologies and databases. As with many walks in life, the most glamorous legacies tend to get the most attention and the least are neglected

"The rapid improvement in DNA sequencing has sparked a big data revolution in genomic sciences, which has in turn led to a proliferation of bioinformatics tools. To date, these tools have encountered little adversarial pressure. This paper evaluates the robustness of such tools if (or when ) adversarial attacks manifest. We demonstrate, for the first time, the synthesis of DNA which - when sequenced and processed - gives an at- tacker arbitrary remote code execution. To study the feasibility of creating and synthesizing a DNA-based exploit, we performed our attack on a modified down- stream sequencing utility with a deliberately introduced vulnerability. "

"From Science Fiction to Reality"

TypeScript is a language for application-scale JavaScript development. TypeScript is a typed superset of JavaScript that compiles to plain JavaScript. Any browser. Any host. Any OS. Open Source.

"Introducing A.I. Experiments. Explore machine learning by playing with pictures, language, music, code, and more."

"The old phrase "united we stand, divided we fall" applies equally well to the mechanisms of attention as it does to a patriotic cause. When devoted to a single task, the brain excels; when several goals splinter its focus, errors become unavoidable... non-stop decision making "

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