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Bullisher is a data centric fintech Solution provider in the aerospace and defense industry for institutional level investors, looking to disrupt and revolutionise a $3 trillion dollar industry. We spearhead an industrial-leading Blackbox to facilitate and administer trade agreements pioneered by a vehicle, driven by our new generation benchmark delivering solutions through innovation with uncompromising agility.
JOB DESCRIPTION:
The oversight requires collaborative computational agents to be designed to act as computer models for a biological neural network integrated withsensation, memory, attention, languages, controls, adaptive behavior, and learning capabilities.A team of seven will deploy biological neural networks to discover applicable features receiving signals from other neurons to generate input integrating data. Ensuring the incoming signals reach the threshold of this transition. Connections can increase or decrease the signals subject to the transmitter. The biological neural network will be a highly interconnected network with learning capabilities.Areas to cover will include:computational engine capable of autonomous actions will require certain objectives: e.g. kinetically perceiving semantic graph that encapsulates skills in the environment through sensors, mapping sequences of perceptions into action. The specification will be integrated with a mathematical structure consisting of a set of environmental states, set ofIOMT, AWUArtificial Wisdom Unit that deduces the strategy expert system into two subsystems (the knowledge-basedKB& inference engineIE).Areas to focus:Apply the rules to known effects to the new rules effects, the wisdom unit corresponds to set actions and replaces the main agents to provide the organization continuity to reconfigure the network to meet the startup purpose. Secure the communication and provide confidentiality and integrity of messages.METHODS TO IMPLEMENT:centralized group key management protocols, decentralized architectures, distributed key management protocols, cryptography-based access controls, intrusion detection and defense likeBBBblock, brain, barrier for the human brain. All team members obtain key generation; all team members can perform access control and generation of the key should be contributory. (All team members contribute some information to generate the group key, which will be done by theHead of Systems Integration, undergoing formal approval, review, and voting by representatives for Security Impact Analysis,THE C.A.B (CHANGE APPROVAL BOARD).) The cryptography-based access controls classical require data protection enforced with cryptography, and the systems that require the data should have a cryptography key to encrypt that data. Secure multi-party computation enabling data scientists, data migration analysts to securely privately compute distributed data without exposing it or moving it e.g. The signature protocol is based on secure multi-party computing (threshold signature, group authentication, self-enforcing authentication, preserved privacy, correctness and verifiability, robustness). Split secrets among team members group signature.METHODS TO IMPLEMENT:Only members of the team can sign messages; the receiver of the signature can verify that it is a valid signature from the team members. The receiver of the signature cannot determine which member of the group is the signer. If required, the signature can be “open” to reveal the identity of the signer. Role distribution among clusters will be implemented in support of distributedHSMto keep the threshold cryptography capabilities.
Strict information security policy will be implemented to truly enforce access control. Safe monitoring ofIOMTnetworks requires trustworthy solutions embedded barriers by embedding available models of biological neural networks.Areas to cover will include:Provide the best suitable for data full exchange in large scale peer-to-peer networks, provide scalability due to load distribution among all nodes in the system, ensure fault-tolerance through an intrinsic level of redundancy that covers node and network failures. The process will be iterative and incremental; it’s an adaptive system designed to simulate dynamic phenomena harnessing finite amounts of data making decisions in real time. Information continuous integration(CI)along withAIpredicate mathematical models network with intellectual methods of collaborative things which will be capable of self-organizing networks of cognitive systems. This number of components may change; the data acquisition might evolve over time. An additional value will drive new requirements and new features - From the edge where command & controls are effective by current conditions all the way to the edge data center where data analytics are performed for example; predictive maintenance, and continuous process improvement. We are a startup enhancing the formation of early stages of a product development project.
OUR KEY OBJECTIVES:
- Develop a core weight directed graph, with minimal knowledge according to Area of complications along with embedded required knowledge provided by appropriate set systems about physical plan under modelings, biological size industrial and epidemiological analysis.
- Trace initial directed paths between vertices (the number of directed between separate clusters to provide information full exchange for additional decision making).
- Encapsulate initial knowledge or skills on clusters of decentralized self-organized subgraphs (overall wisdom).
- Provide secure communication between clusters (The number of bridges connecting the clusters will be reasonably small to avoid unpredictability of information exchange).
- Map & reduce large amounts of data collected.
- Develop fault-tolerant protocols for information full exchange between clusters.
- Adjust decision making according to success and failure.
- Classify possible network attacks specific to the chosen network model.
- Launch secure multi-party computation.
- Exclude the role of Arbitrators (Third Trusty Party) to complete the protocol.
ENVIRONMENT:This position will operate in the following areas of the organization regulatory engineering divisionMULTIDOMAIN DEFENCE DOCK:
MULTIDOMAIN DEFENCE DOCKStandard engineering lab environment.
Employees must be legally authorized to work in theUK. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position.
QUALIFICATION, KEY REQUIREMENTS AND SKILLS SET:
- PHDin Mathematical Cryptography & Computer Science/ Computer Systems Engineering.
- 10+ years in-depth working knowledge in Artificial Cognitive Systems & Biological Neural Networks(BNN).
- Strong programming skills inC++andRust.
- Offensive Security Certified Professional(OSCP).
- Certified Information Security Manager(CISM).
- Information Systems Security Engineering Professional(ISSEP)is essential.
- Certified Authorization Professional(CAP).
- Certified Network Defender(CND).
- Information Assurance System Architecture and Engineer(IASAE).
- It’s a prerequisite to be certified in one of the listedDoD 8570Certifications.
INTERVIEW PROCESS:
- STAGE 1:COGNITIVE ABILITY TEST
- STAGE 2:COGNITIVE ASSESSMENT SCREENING: WITH A 30+ YEARS EXPERIENCE PSYCHOLOGIST.
- STAGE 3:PRE-SCREENING (verification checks & DV security clearance)
- STAGE 4:INTERVIEW WITH THE: CEO & CTO
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