| Category | Message to Investors |
|---|---|
| Onsor partnership | Validates Akida in medical‑grade, real‑time, ultra‑low‑power applications (seizure‑detecting glasses). Shows the tech works where latency and power matter most. |
| Frontgrade Gaisler integration | Confirms Akida is suitable for radiation‑hardened, space‑grade SoCs. Space customers only adopt tech that is extremely reliable and efficient. |
| Parsons / Blue Ridge Envisioneering | Demonstrates traction in defense edge‑AI systems, where ruggedness, low power, and real‑time inference are mandatory. |
| Bascom Hunter / Navy SBIR | Shows adoption in DoD‑aligned programs and rugged neuromorphic hardware (SNAP card). Their purchase of Akida‑1500 chips signals real demand. |
| RTX / USAFRL expansion | A major aerospace/defense contractor expanding trials after “surprising results” is a strong technical endorsement. Suggests Akida outperformed expectations. |
| Akida‑1500 tape‑out | Indicates BrainChip is executing, not just promising. Tape‑out means the design is real, stable, and ready for manufacturing. |
| GenAI roadmap | Shows BrainChip is aligning with the biggest trend in tech: on‑device generative AI. Signals long‑term relevance. |
| Gen3 architecture direction | Confirms BrainChip is not a one‑chip company. A maturing roadmap increases investor confidence in future revenue streams. |
| Multiple partnerships across sectors | Demonstrates broad applicability: medical, defense, aerospace, industrial, space, consumer. Reduces single‑market risk. |
| Customers requesting chips before licensing | Strongest sign of commercial readiness. Customers want hardware validation before committing to IP licensing — a normal path toward high‑margin revenue. |
| Hearing‑aid adoption accelerating | Hearing aids are a high‑volume consumer vertical. Early adoption signals potential for recurring, scalable revenue. |
Hi Fmf,Seems Honeywell through their owned CAES entity are another defence player looking to develop neuromorphic/ cognitive and would like someone preferably with experience with the likes of Akida / Loihi hardware. My bold.
7 Seven Bridges jobs in United States
Today's top 7 Seven Bridges jobs in United States. Leverage your professional network, and get hired. New Seven Bridges jobs added daily.www.linkedin.com
Artificial Intelligence & Machine Learning Systems Engineer-Cognitive Electronic Warfare (EW)
CAES San Jose, CA
1 week ago Be among the first 25 applicants
Delivering mission-critical, electronic solutions that protect lives. Use your creativity and critical thinking to take our products from concept to customer.
At CAES by Honeywell, we engineer solutions for the world’s most critical missions. We serve customers in the defense and aerospace markets. Seeking a career that offers challenging, diverse projects and opportunities? Looking for a position with a company that offers long-term professional advancement? Searching for a place that values a diverse, team-based environment? One that values YOU. Consider CAES by Honeywell.
The most important thing we build is TRUST
#CustomerFocus #Values #Leader #TogetherWePioneer
Overview
We’re seeking a highly skilled Artificial Intelligence & Machine Learning Systems Engineer to architect, design, and develop advanced AI/ML systems that power our next generation of products. In this leadership role, you’ll contribute to the technical roadmap, mentor engineering teams, and collaborate with cross-functional teams to deliver intelligent, scalable, and production-ready AI and machine learning technologies. You will be responsible for researching, creating, adapting and evaluating AI/ML techniques to solve complex customer problems with real-time solutions to support our defense customers.
Specifically, we are building next-generation cognitive electronic warfare systems that operate autonomously at the tactical edge in contested, low-SWaP (Size, Weight, and Power), denied, and disconnected environments. This is not a prompt-engineering or GenAI role. We are looking for hardcore AI/ML systems engineers who treat machine learning as a component of a larger, mission-critical, real-time embedded system.
Responsibilities
Major Duties & Responsibilities:
Qualifications
- Design, implement, and harden on-line and continual-learning ML algorithms for RF signal classification, adaptive jamming, cognitive radar, and electronic attack/support decision engines.
- Port, optimize, and deploy ML inference algorithms to edge processors.
- Build and maintain low-latency, deterministic inference pipelines that integrate tightly with real-time RF front-ends and digital signal processing chains.
- Lead the systems integration of AI/ML techniques into mission-critical embedded platforms running real-time operating systems.
- Design and deliver warfighter-focused engineering visualizations and tactical displays (real-time spectrum awareness, threat emitter tracks, cognitive EW decision overlays, confidence heatmaps) using modern web stack frameworks that run natively on embedded tactical processors and dismounted soldier systems.
- Own the MLOps and DevSecOps pipeline for classified EW programs: secure CI/CD, model versioning, containerized build/test/deploy, SBOM generation, and compliance with DoD zero-trust and CNCF security standards.
- Architect and deploy Kubernetes-based edge orchestration clusters (e.g. k3s) that operate in fully air-gapped tactical environments with strict latency and availability requirements.
- Perform end-to-end performance profiling (memory bandwidth, cache coherency, DMA, GPU/TPU/NPU utilization).
- Review code, guide architecture decisions, and mentor the AI/ML engineering team.
- Collaborate with product and engineering teams to identify AI/ML-driven opportunities.
Required Qualifications:
Preferred Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or related field
- 7+ years of professional experience shipping production AI/ML systems, ideally in defense, aerospace, or autonomous systems
- Prior work on DoD cognitive EW programs
- Deep expertise in high-performance and real-time applications (not just scripting wrappers)
- Real-time and embedded application programming (no Python-only backgrounds)
- Proven track record of deploying AI/ML solutions to cloud and edge/constrained devices
- Strong systems engineering background: you understand clocks, interrupts, DMA, cache hierarchies, memory-mapped I/O, and real-time scheduling
- Hands-on experience building and securing CI/CD pipelines for classified or regulated environments
- Expertise with Docker, container hardening, and Kubernetes in disconnected/edge configurations (k3s, microk8s, Rancher Harvester).
- Familiarity with RF/ML intersections: signal detection & classification, modulation recognition, emitter geolocation, fingerprinting, adaptive waveform design, or reinforcement learning for EW
- Proficiency with ML algorithms (including NLP, Computer Vision, time-series), libraries including foundational understanding and expertise in statistics probability theory and linear algebra
- Strong understanding of machine learning fundamentals: supervised/unsupervised learning, deep learning, model evaluation, optimization, feature engineering, etc
- Experience with data engineering workflows and building robust training datasets
- Experience as the technical lead for establishing and accrediting classified AI/ML information systems under the DoD Risk Management Framework (RMF):
- Author and maintain System Security Plans (SSP), Security CONOPS, and AI/ML-specific risk annexes
- Build and harden multi-enclave classified development, integration, and operational environments (RHEL 8/9, SELinux enforcing, DISA STIGs, Assured Compliance Assessment Solution (ACAS))
- Lead the creation of AI/ML-specific artifacts for eMASS packages, including model cards, data provenance, adversarial robustness testing, and continuous monitoring plans
- Obtain and maintain Authority to Operate (ATO) for classified cognitive EW systems containing advanced GPU/NPU-accelerated AI infrastructure
- Perform Linux systems administration at the classified level: kernel tuning for real-time determinism, custom security hardening, cross-domain solution integration, auditd/ELK stack management, and FIPS 140-3 compliant cryptography
- Deep Linux systems administration and hardening experience in classified environments (RHEL/CentOS, STIG compliance, SELinux policy authoring).
- Hands-on experience authoring RMF packages and obtaining ATOs for systems containing machine learning components for the U.S. Government (Army, Navy, Air Force, or IC customer)
- Expertise with Docker, container hardening (CIS, OSCAP), and Kubernetes in disconnected tactical environments
- Experience or exposure with implementing Government reference architectures
- Experience with neuromorphic or spiking neural network hardware (Intel Loihi, BrainChip Akida)
- Experience with distributed training, GPU acceleration, and high-performance ML compute
- Strong background in foundation algorithms, transformers, or multimodal AI
- Knowledge of automated model monitoring, drift detection, and lifecycle management
- Experience integrating ML models into consumer or enterprise products
Olivier Coenen entered the chatSeems Honeywell through their owned CAES entity are another defence player looking to develop neuromorphic/ cognitive and would like someone preferably with experience with the likes of Akida / Loihi hardware. My bold.
7 Seven Bridges jobs in United States
Today's top 7 Seven Bridges jobs in United States. Leverage your professional network, and get hired. New Seven Bridges jobs added daily.
Artificial Intelligence & Machine Learning Systems Engineer-Cognitive Electronic Warfare (EW)
CAES San Jose, CA
1 week ago Be among the first 25 applicants
Delivering mission-critical, electronic solutions that protect lives. Use your creativity and critical thinking to take our products from concept to customer.
At CAES by Honeywell, we engineer solutions for the world’s most critical missions. We serve customers in the defense and aerospace markets. Seeking a career that offers challenging, diverse projects and opportunities? Looking for a position with a company that offers long-term professional advancement? Searching for a place that values a diverse, team-based environment? One that values YOU. Consider CAES by Honeywell.
The most important thing we build is TRUST
#CustomerFocus #Values #Leader #TogetherWePioneer
Overview
We’re seeking a highly skilled Artificial Intelligence & Machine Learning Systems Engineer to architect, design, and develop advanced AI/ML systems that power our next generation of products. In this leadership role, you’ll contribute to the technical roadmap, mentor engineering teams, and collaborate with cross-functional teams to deliver intelligent, scalable, and production-ready AI and machine learning technologies. You will be responsible for researching, creating, adapting and evaluating AI/ML techniques to solve complex customer problems with real-time solutions to support our defense customers.
Specifically, we are building next-generation cognitive electronic warfare systems that operate autonomously at the tactical edge in contested, low-SWaP (Size, Weight, and Power), denied, and disconnected environments. This is not a prompt-engineering or GenAI role. We are looking for hardcore AI/ML systems engineers who treat machine learning as a component of a larger, mission-critical, real-time embedded system.
Responsibilities
Major Duties & Responsibilities:
Qualifications
- Design, implement, and harden on-line and continual-learning ML algorithms for RF signal classification, adaptive jamming, cognitive radar, and electronic attack/support decision engines.
- Port, optimize, and deploy ML inference algorithms to edge processors.
- Build and maintain low-latency, deterministic inference pipelines that integrate tightly with real-time RF front-ends and digital signal processing chains.
- Lead the systems integration of AI/ML techniques into mission-critical embedded platforms running real-time operating systems.
- Design and deliver warfighter-focused engineering visualizations and tactical displays (real-time spectrum awareness, threat emitter tracks, cognitive EW decision overlays, confidence heatmaps) using modern web stack frameworks that run natively on embedded tactical processors and dismounted soldier systems.
- Own the MLOps and DevSecOps pipeline for classified EW programs: secure CI/CD, model versioning, containerized build/test/deploy, SBOM generation, and compliance with DoD zero-trust and CNCF security standards.
- Architect and deploy Kubernetes-based edge orchestration clusters (e.g. k3s) that operate in fully air-gapped tactical environments with strict latency and availability requirements.
- Perform end-to-end performance profiling (memory bandwidth, cache coherency, DMA, GPU/TPU/NPU utilization).
- Review code, guide architecture decisions, and mentor the AI/ML engineering team.
- Collaborate with product and engineering teams to identify AI/ML-driven opportunities.
Required Qualifications:
Preferred Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or related field
- 7+ years of professional experience shipping production AI/ML systems, ideally in defense, aerospace, or autonomous systems
- Prior work on DoD cognitive EW programs
- Deep expertise in high-performance and real-time applications (not just scripting wrappers)
- Real-time and embedded application programming (no Python-only backgrounds)
- Proven track record of deploying AI/ML solutions to cloud and edge/constrained devices
- Strong systems engineering background: you understand clocks, interrupts, DMA, cache hierarchies, memory-mapped I/O, and real-time scheduling
- Hands-on experience building and securing CI/CD pipelines for classified or regulated environments
- Expertise with Docker, container hardening, and Kubernetes in disconnected/edge configurations (k3s, microk8s, Rancher Harvester).
- Familiarity with RF/ML intersections: signal detection & classification, modulation recognition, emitter geolocation, fingerprinting, adaptive waveform design, or reinforcement learning for EW
- Proficiency with ML algorithms (including NLP, Computer Vision, time-series), libraries including foundational understanding and expertise in statistics probability theory and linear algebra
- Strong understanding of machine learning fundamentals: supervised/unsupervised learning, deep learning, model evaluation, optimization, feature engineering, etc
- Experience with data engineering workflows and building robust training datasets
- Experience as the technical lead for establishing and accrediting classified AI/ML information systems under the DoD Risk Management Framework (RMF):
- Author and maintain System Security Plans (SSP), Security CONOPS, and AI/ML-specific risk annexes
- Build and harden multi-enclave classified development, integration, and operational environments (RHEL 8/9, SELinux enforcing, DISA STIGs, Assured Compliance Assessment Solution (ACAS))
- Lead the creation of AI/ML-specific artifacts for eMASS packages, including model cards, data provenance, adversarial robustness testing, and continuous monitoring plans
- Obtain and maintain Authority to Operate (ATO) for classified cognitive EW systems containing advanced GPU/NPU-accelerated AI infrastructure
- Perform Linux systems administration at the classified level: kernel tuning for real-time determinism, custom security hardening, cross-domain solution integration, auditd/ELK stack management, and FIPS 140-3 compliant cryptography
- Deep Linux systems administration and hardening experience in classified environments (RHEL/CentOS, STIG compliance, SELinux policy authoring).
- Hands-on experience authoring RMF packages and obtaining ATOs for systems containing machine learning components for the U.S. Government (Army, Navy, Air Force, or IC customer)
- Expertise with Docker, container hardening (CIS, OSCAP), and Kubernetes in disconnected tactical environments
- Experience or exposure with implementing Government reference architectures
- Experience with neuromorphic or spiking neural network hardware (Intel Loihi, BrainChip Akida)
- Experience with distributed training, GPU acceleration, and high-performance ML compute
- Strong background in foundation algorithms, transformers, or multimodal AI
- Knowledge of automated model monitoring, drift detection, and lifecycle management
- Experience integrating ML models into consumer or enterprise products
Evidently the idea is you take action as prescribed eg medication, rest, hydration, care with exposure to light until the signal stops. So once you get a signal its trigger avoidance time.Interesting… but what could someone do against migraine? Take the pill earlier?
biologyinsights.com
| Condition | How Common? | Notes |
|---|---|---|
| Migraine (overall) | 6.6% of Australians (1.7M people) | One of the most disabling neurological conditions |
| Silent migraine | ~5% of migraine sufferers | Aura symptoms without headache |
I know my way around migraines really well. But I honestly can’t imagine how early detection would help me.Evidently the idea is you take action as prescribed eg medication, rest, hydration, care with exposure to light until the signal stops. So once you get a signal its trigger avoidance time.
Migraines can very painfull and its not uncommon for a migraine to last several days or more. They can lead to a hospital stay.
There are also silent migraines do not have the same 'head throbbing' experience as migraines but cause alarming symptoms such as nausea, sight issues, dizziness, head fog etc.
Its also a huge global problem.
"Migraine Prevalence: Global and Demographic Statistics
Gain a deeper understanding of migraine’s global footprint and its varied impact across different groups, underscoring its broad public health implications.
Global and Regional Occurrence
In 2019, the estimated global prevalence of migraine reached 1.1 billion cases, an increase from 721.9 million cases in 1990. This translates to approximately 14,246.55 cases per 100,000 population in 2021. Headache disorders, including migraines, collectively affect about 40% of the global population, or 3.1 billion people."
My bold above. As shown above its a massive global problem as shown above.
This has the potential to be huge for Brainchip. Also take into account Steve's off the shelf ear buds/hearing aids the Brainchip future in health is starting to look fair at the least.
For the Australian experience see quick comparable chart compiled by 'chat' with links below.
Quick comparison table
Condition How Common? Notes Migraine (overall) 6.6% of Australians (1.7M people) One of the most disabling neurological conditions Silent migraine ~5% of migraine sufferers Aura symptoms without headache
Chat above provides a quick comparison table with just for Australia. links
Silent migraines - https://www.migraine.org.au/mawh?utm_source=copilot.comNeurological conditions in Australia: Migraine and headaches in Australia
Migraine and headache disorders are among the most common chronic neurological conditions. Migraine is a specific headache disorder characterised by recurrent attacks of severe headache...www.aihw.gov.au
Evidently around 75% of migraines are suffered by women. Its a huge problem
Here's a very fair question, if you believe that our company will finally, yes finally show revenues that outpace expenses by the 3rd quarter of 2026, no matter from what source, for example, high speed boats that Donald's boy failed to stop across the Pacific etc, well please post a laugh....
Just trying to lighten the mood, many are feeling the pressure of the market at present, that's a very fair assumption.
To be perfectly honest, with no revenue and capital raisings, it's sort of understandable that we currently sit south of 0 20...hang in there, progress is being made and yes, I'm quietly peeved as well.
Tech
It have family that suffer from migraines and hospital visits are not uncommon. They are 'shockers'.
Reading up on migranes there are studies that lean towards nutrition or the lack of certain minerals that when increased reduce the amount of migrane.
A massive rise of the share price would already help IMO… no clinical studies no medication
$2 would be nice for starters. Only starters though.A massive rise of the share price would already help IMO… no clinical studies no medication
Oh man this would be my medication… 2 dollars a share for beginning
