NETWORK INTEGRATION ENGINEER (ARTIFICIAL COGNITIVE SYSTEMS)-AEROSPACE AND DEFENSE:

(INTELLIGENT NETWORK MODELS)

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. Predicts trends in the aerospace and government defense entities, predicts trends in political shifts and the ability to influence actual effect changes in government policies through innovation.

JOB DESCRIPTION:

The oversight requires a collaborative computational agents to be designed to act as computer model for a biological neural network which are integrated with (sensation, memory, attention, languages, controls, adaptive behavior, and learning capabilities). A team of seven- will deploy biological neural to discover applicable features receiving signals from other neuron’s to generate input integrating data. Ensuring the coming signals reached the threshold of this transition. Connections can increase or decrease the signals subject to the transmitter. The biological neural network will be highly interconnected network with learning capabilities. Areas to cover will include: computational engine capable of autonomous actions will require certain objective: e.g kinetically perceiving semantic graph that encapsulates skills in environment through sensors, mapping sequences of perceptions into action. The specification will be integrated with a mathematical structure consist of set of environmental states, set of IOMT, AWU Artificial wisdom unit that deduces the strategy expert system into two subsystems (the knowledge based KB & inference Engine IE). Areas to focus: Apply the rules to known effects to the new rules effects, the wisdom unit correspond to set actions and replace the main agents in order to provide the organization continuity to reconfigure the network to get new configuration 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 like BBB blood, brain, barrier for human brain,. All the team members obtain the 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 will be done by the Head of systems integration), will undergo a formal approval, review and voted by representatives for Security impact analysis, THE C.A.B (CHANGE APPROVAL BOARD). The cryptography based access controls classical requires data protection enforce with cryptography and the systems that require the data should have a cryptography key to encrypt that data. Secure multi-party computation enabling data scientist’s, data migration analyst 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 member’s, 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 distributed HSM to keep the threshold cryptography capabilities.

Strict information security policy will be implemented in order to truly enforced access control. Safe monitoring of IOMT networks requires trusty  solutions embedded barriers by embedding available models of biological neural network. 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 systems that are designed to simulate dynamic phenomenon, gathering data, making decisions in real time. Information continuous integration (CI) along with AI predicate mathematical models network with intellectual methods of collaborative things which will be capable to act as self Organized networks off the 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 way to the edge data centre 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 a minimal knowledge according to Area of applications 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 edges 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)- similar to the human body with different internal organs each having all functionality and capability.
• Provide secure communication between clusters (The number of bridges connecting the clusters will be reasonably small in order to avoid unpredictability of information exchange).
• Map & reduce large amount 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 division MULTIDOMAIN DEFENCE DOCK:

MULTIDOMAIN DEFENCE DOCK“ Standard engineering lab environment ”

Employees must be legally authorised to work in the UK. 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:

• PHD in Mathematical cryptography & Computer Science/ Computer systems Engineering
• 10yrs+ In-depth working knowledge in Artificial Cognitive systems & Biological neural networks BNN.
• A strong programming skills in C++ and Rust.
• 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)
• Its prerequisite to be certified one of the listed DoD 8570 Certifications.

INTERVIEW PROCESS:

• STAGE 1: COGNITIVE ABILITY TEST
• STAGE 2: COGNITIVE ASSESSMENT SCREENING: WITH A 30yrs+ EXPERIENCE PSYCHOLOGIST:
• STAGE 3: PRE-SCREENING (verification checks & DV security clearance)
• STAGE 4: INTERVIEW WITH THE: CEO & CTO