The EE-FE works with the FBI Embedded Engineering Program EEP at the Operational Technology Division — a world-class microelectronics facility that serves as the last stop for the examination of electronic devices — to ensure when electronic devices are encountered, the appropriate actions are taken to ensure the evidence is fully and accurately understood. For more information or to see if you qualify as an Electronic Engineer with the FBI, please send resume and transcripts to techworkforce ic.
Electronic Surveillance ELSUR Operations Technicians EOTs are responsible for maintaining physical electronic surveillance evidence, managing the inventory and disposition of electronic surveillance evidence and reporting on activities.
EOTs provide advice and assistance to case agents regarding statutory and compliance requirements for evidence control and the reporting associated with electronic surveillance operations. Interested EOT candidates must have a high school diploma, basic computer and administrative skills, strong customer service and communication skills.
This position could work in all 56 field offices. Electronics Technician roles range from working with radio frequency systems to data networks and offering tactical support.
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Electronics Technicians should possess basic electronics knowledge, either through experience or training, preferably with certification, knowledge or skills in the following: DC circuits, AC circuits, solid-state devices, digital circuits, integrated circuits, microprocessors, microcontrollers and programmable logic controllers. For more information or to see if you qualify as an Electronics Technician with the FBI, please send resume and transcripts to techworkforce ic.
Based primarily at the criminal justice Information Services Division and within the Information Technology Branch, IT Specialists are the critical backbone that ensures FBI law enforcement systems are up to date and available for use by FBI field offices, as well as domestic and international law enforcement partners.
IT Specialists — Forensic Examiners ITS-FEs provide comprehensive forensic examinations and technical analysis of computer-related digital evidence and provide technical guidance and assistance to others involved in investigations to ensure precautions are taken to prevent data and equipment damage. For more information or to see if you qualify as an Information Technology Specialists with the FBI, please send resume and transcripts to techworkforce ic.
Click here to find out more about becoming a Special Agent. One day may require a terrorist to be identified though a partial fingerprint or a single strand of hair.
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Other tasks may require knowledge of advanced computer systems to monitor the communications of suspected criminals or spy organizations. An FBI STEM career will help applicants stand out by exposing them to work unlike any other offered by a government agency or private-sector company. Skip to main content. Apply to Jobs. Using Innovation to Protect the Nation. You can disable the usage of cookies by changing the settings of your browser.
By browsing our website without changing the browser settings you grant us permission to store that information on your device. Neuromorphic computing implements aspects of biological neural networks as analogue or digital copies on electronic circuits. The goal of this approach is twofold: Offering a tool for neuroscience to understand the dynamic processes of learning and development in the brain and applying brain inspiration to generic cognitive computing. Key advantages of neuromorphic computing compared to traditional approaches are energy efficiency, execution speed, robustness against local failures and the ability to learn.
In the HBP the neuromorphic computing Subproject carries out two major activities: Constructing two large-scale, unique neuromorphic machines and prototyping the next generation neuromorphic chips. The large-scale neuromorphic machines are based on two complementary principles. The many-core SpiNNaker machine located in Manchester UK connects 1 million ARM processors with a packet-based network optimized for the exchange of neural action potentials spikes. The BrainScaleS physical model machine located in Heidelberg Germany implements analogue electronic models of 4 Million neurons and 1 Billion synapses on 20 silicon wafers.
Both machines are integrated into the HBP collaboratory and offer full software support for their configuration, operation and data analysis. The most prominent feature of the neuromorphic machines is their execution speed.
The SpiNNaker system runs at real-time, BrainScaleS is implemented as an accelerated system and operates at 10, times real-time. Simulations at conventional supercomputers typical run factors of slower than biology and cannot access the vastly different timescales involved in learning and development ranging from milliseconds to years. Recent research in neuroscience and computing has indicated that learning and development are a key aspect for neuroscience and real world applications of cognitive computing.
HBP is the only project worldwide addressing this need with dedicated novel hardware architectures. The BrainScaleS system is based on physical analogue or mixed-signal emulations of neuron, synapse and plasticity models with digital connectivity, running up to ten thousand times faster than real time. The SpiNNaker system is based on numerical models running in real time on custom digital multicore chips using the ARM architecture. More Information about the machines and the next generation plans and development is available in the How we work - Hardware section.
A usage graph as of December is included in this new item. A number of demonstrations of the benefits of neuromorphic technology are beginning to emerge , and more can be expected in the short to medium term. Various start-up companies are emerging, in the USA and elsewhere, to exploit the prospective advantages of neuromorphic and similar technologies in these new machine learning application domains.
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In the HBP, small and large-scale demonstration systems are available and attract an increasing number of users from industry and academia. While these systems are primarily made for basic research on understanding information processing in the human brain, efforts are being made to also implement machine learning tasks on them. Next generation small scale test chips of the SpiNNaker and BrainScaleS architecture are available for first test users since early In the medium term we may expect neuromorphic technologies to deliver a range of applications more efficiently than conventional computers, for example to deliver speech and image recognition capabilities in smart phones.
Currently such capabilities are available only using powerful cloud resources to implement the recognition algorithms. These will require small-scale neuromorphic accelerators integrated with the application processor, using a fraction of the resources of a single chip.