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supersonic001
Regular
BrainChip on LinkedIn: BrainChip Details Olfactory Capabilities of Identifying Bacteria in the…
BrainChip Details #Olfactory Capabilities of Identifying Bacteria in the Blood in New Research Report - Details how #neuromorphic #computing can be utilized as…
Frangipani
Regular
Also, serendipitously, I just happened to discover this November 2023 investor presentation online (and since none of its slides are marked ‘confidential’, I picked out my favourite ones… 
), which appears to be aimed at US investors, due to the listing of the ADRs (OTCQX: BCHPY) preceding that of the shares (ASX: BRN).
), which appears to be aimed at US investors, due to the listing of the ADRs (OTCQX: BCHPY) preceding that of the shares (ASX: BRN).
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Learning
Learning to the Top 🕵♂️
Thanks for sharingGreat find, @thelittleshort !
Since the link to X-formerly-known-as-Twitter didn‘t work for me, I googled for the info elsewhere and found this research paper on the Brainchip website:
Finding Bacteria in Blood: A Hardware-based Neuromorphic Solution for Real-World E-Nose Applications
Explore the revolutionary application of neuromorphic technology in creating an electronic nose for real-world scenarios.brainchip.com
Finding Bacteria in Blood
A Hardware-based Neuromorphic Solution for Real-World E-Nose Applications
Executive Summary:
Advancements in neuromorphic technology, which mimics the human brain has opened exciting possibilities in creating machines that can smell, much like our noses. This paper introduces a bio-inspired, low-power method that recognizes different patterns of scents, functioning as the “brain” of an electronic nose. This highly efficient and compact solution can be seamlessly incorporated into electronic nose systems targeted for high-precision, real-world applications. In practical tests, it has proven very effective, especially in swiftly identifying various bacteria in blood samples using electronic nose data. The proposed method achieves 181 inferences per second with a classification accuracy of 97.42% and dynamic efficiency, measured as energy consumed per inference, of 135 µJ/inference. This combination of accuracy, efficiency, and speed makes it an exceptional tool for health and safety applications that can be used in a cost-effective, portable form factor in remote regions, which may not have access to labs or even connectivity to the cloud.
Advancing Artificial Olfaction: Bridging Biology and Technology
Electronic noses (e-noses) serve a vital role in applications such as quality assurance in food production and non-invasive medical diagnostics. They are sophisticated devices designed to detect odours. They operate by mimicking the biological process of smell, where specific neurons in the nose identify volatile organic compounds and send encoded signals to the brain for odour recognition. Electronic noses are equipped with an array of chemical sensors that produce electrical signals when they encounter airborne molecules, known as Volatile Organic Compounds (VOCs). The unique pattern of these signals is akin to a fingerprint, which can be analysed and identified using various techniques to determine the specific odour present.
Despite the technological prowess demonstrated in laboratory settings, the use of electronic noses in everyday environments faces challenges. Limitations include the need for greater portability, improved power efficiency, enhanced performance, and quicker, more reliable odour classification. Advances in machine learning have increased the accuracy of electronic noses; however, these improvements often come at the cost of increased computational demand, affecting the device’s portability and energy usage. Current research in neuromorphic computing, inspired by the brain’s efficient processing, promises to overcome these limitations by emulating the accuracy and low-energy profile of the natural olfactory system. While previous studies have achieved intricate biological mimicry, such complexity is not practical for widespread application.
Our research presents a bio-inspired approach that harnesses the efficiency of neuromorphic hardware. We have developed a neuromorphic system that processes electronic nose data using a combination of an event-based data encoder and a spiking neural network classifier optimized for implementation on Akida neuromorphic hardware. This system is built to leverage the inherent strengths of neuromorphic computing, aiming to provide a robust inference engine capable of high-accuracy odour classification with low power consumption and minimal delay, ready for real-world deployment.
Overview:
In this research, we utilized the ‘bacteria in blood’ dataset, which was previously collected by the Mednose project team at Örebro University. Their project was centred on the early detection of different bacterial species in blood, by identifying the unique chemical signatures they emit during initial growth phases. The collection was carried out using the NST 3220 Emission Analyzer, a sophisticated electronic nose device developed by Applied Sensors, equipped with a combined array of 22 metal-oxide semiconductor (MOS) and MOSFET sensors. The project’s objective was to distinguish between ten types of bacteria in blood samples, and for this purpose, a comprehensive dataset of 1200 samples was created, with 120 samples representing each bacterial species. For an in-depth understanding of the electronic nose experiments and the sampling protocols, a detailed description is available in the work by Trincavelli et al. titled “Direct identification of bacteria in blood culture samples using an electronic nose,” published in the IEEE Transactions on Biomedical Engineering
Streamlining Data: From Raw Signals to Event-Driven Patterns
Pre-processing plays a pivotal role in transforming raw complex sensor data into a more manageable form for pattern recognition and analysis. We start by removing any constant background noise from the readings—a step known as baseline cancellation. Then we adjust the scale of the sensor responses to ensure consistency across all samples.
The heart of our approach is a specialized model designed to interpret these streamlined signals. Our model is fine-tuned for data that’s been transformed into a pattern of digital events, similar to the way our brain receives and processes sensory input. To convert the sensor’s continuous data into this digital format, we explored two methods. The first method, based on our previously published encoding algorithm known as Address Event Representation for Olfaction (AERO), arranges the sensor information into a structured grid, marking the presence of a signal at each moment in time. This is akin to creating a digital snapshot of the scent at every point, which helps us capture the essential characteristics of each odour.
The second method, the Step Forward (SF) algorithm, a bio-inspired data encoding method, highlighting changes in the odour data only when they pass a certain threshold. This is inspired by how our senses work, paying attention to changes rather than static information, making it efficient and effective at capturing rapid variations in odour signals.
We also considered that not all data might be necessary for accurate identification. Perhaps, after the initial burst of information when the scent is first detected, the remaining data might not add much value. So, we experimented with using different amounts of data to see if we could still identify the odours without the full 5-minute recording, focusing only on the critical moments that define each scent.
Optimising Akida Spiking Neural Networks for Effective Odour Detection
The core of our classification system is a spiking neural network (SNN), which mimics the way neurons in the brain process information and learn. It consists of two main layers: the input layer, where data comes in, and the processing layer, where the learning happens. The neurons within the processing layer are finely tuned to learn and recognize patterns in the incoming data and respond by firing when a familiar pattern is identified, thereby signalling the detection of a specific odour.
The learning mechanism of our SNN is rooted in a biological process known as Spike Time Dependent Plasticity (STDP). This natural learning mode allows the network to rapidly strengthen the synaptic connections that correspond to frequently encountered activation patterns, a fundamental aspect of how the brain learns from repetition and reinforcement. In the classification phase, the system employs a competitive process where the most activated neuron—indicating the recognition of a learned pattern—determines the odour’s classification. This is achieved through the winner-take-all approach, where the ‘winning’ neuron’s response guides the output classification.
To achieve the most accurate and efficient performance, we conducted a comprehensive optimisation of the network’s hyper-parameters. This involved adjusting the number of neurons associated with each class of odour, the number of active connections each neuron should have, and the intensity of learning competition among neurons. The configuration resulted in a robust model that could accurately classify odours based on the patterns it had learned.
The SNN model was rigorously tested and validated using MetaTF, a Python-based simulation environment for AKD1000 reference SoC, before its deployment on the Akida reference hardware. This validation ensured that our approach was sound and could be effectively transferred to the NSoC, allowing for a seamless transition from theory to application. The entire workflow, from the initial data pre-processing to the final odour classification, is designed to be fully compatible and integrated within the NSoC, showcasing the system’s readiness for deployment in electronic nose devices.
Performance Evaluation of Akida SNN Classifier
The objective of this study was to evaluate whether the spiking neural network (SNN) could accurately classify odours without needing to process the entire 5-minute sample, thus potentially reducing the time for analysis. We used a windowed approach, where the electronic nose data and then converted into an event-based format by the data-to-event encoder for the SNN to process.
Our performance assessment of the SNN classifier utilised a robust 3-fold cross-validation method, ensuring a reliable statistical evaluation. The results, summarized in the classification table, showed high mean classification accuracies for both encoding algorithms (AERO and Step Forward), particularly with the first 200 data points. This suggests that the essential features for odour differentiation are present early in the sampling process.
The Step Forward encoding technique, while similar in classification accuracy to AERO, was more data-efficient, condensing the information into a smaller input size for the SNN. However, this efficiency required additional pre-processing, which could slightly increase the time to reach a classification result.
For real-world application, the classifiers were tested on the Akida Neuromorphic System-on-Chip (NSoC). The SNN classifiers performed on par with the MetaTF chip emulator used during development. The AERO-based classifier model demonstrated a dynamic power consumption of only 24.5 mW with a high throughput of 181 inferences per second, translating to an energy efficiency of 135 µJ per inference. On the other hand, the Step Forward encoded model consumed slightly more power, averaging 25 mW, with a lower throughput of 31.5 inferences per second, resulting in a dynamic efficiency of 822 µJ per inference.
These differences in performance and efficiency are attributed to the varying neural resources each model utilized. Nonetheless, the findings affirm the potential of SNN classifiers to function as low-power, real-time pattern recognition engines for electronic nose
systems, providing a balance of speed, accuracy, and energy efficiency.
Revolutionizing Diagnostic Tools: Akida’s NSoC-Powered E-Nose
Figure 1: Shows the Mednose apparatus with Applied Sensor NST320 and attached computer.
The system used to develop this Mednose system is fixed, large, power-hungry and needs a great deal of compute. While there are portable systems available for the sensing, they are not capable of doing the diagnostics on device today.
The quest for a real-time, energy-efficient system for odour recognition has long stood as a critical challenge in the development of electronic nose technologies. This paper has showcased a significant leap forward in this domain: the application of an event-based neuromorphic approach for processing and classifying electronic nose data, marking a substantial innovation in pattern recognition engines for e-nose systems.
Fig 2: Sample diagnostic platform using NoseChip 32-array sensor and AKD1000 SoC which can enable single board, extremely compact, energy-efficient, and accurate solution.
In summary, the proposed solution integrates two key components: an innovative data-to-event encoder using AERO and Step Forward (SF) techniques for translating sensor data into a neural-compatible format and an Akida SNN classifier that employs biologically inspired algorithms to discern and categorize patterns in the data.
The performance of this system is notable—when tested on the MedNose dataset, it demonstrated an impressive ability to distinguish among ten different bacteria types, achieving a peak classification accuracy of 97.42%. This not only surpasses previous models, which needed multiple samples to reach slightly lower accuracy levels, but also does so with remarkable efficiency. Critically, the SNN model was rigorously tested on the Akida NSoC, where it exhibited exceptionally low power usage, consuming only 24.5 mW, while maintaining a high throughput of 181 inferences per second. This performance marks a significant advancement, indicating that such a system is not only possible but now can be provided with a very cost-effective and portable device that enables rapid disease diagnosis with the critical level of precision.
These results highlight the processing capabilities of the Akida SNN for complex time-varying signals and demonstrate potential impact of our company’s technology in real-world applications. When used in conjunction with such sensing systems, the implications for healthcare are particularly profound, where these advancements could drastically reduce the time and resources required to assist in disease detection and identification, ultimately leading to faster, more accurate medical responses and better patient outcomes.
Click here to read the White paper for more details.
What a fantastic achievement for AKD1000.
"The SNN model was rigorously tested and validated using MetaTF, a Python-based simulation environment for AKD1000 reference SoC, before its deployment on the Akida reference hardware."
And who was it, that keep telling us AKD1000 was a not good enough! As time goes on AKD1000 had proven otherwise.
Day like this. It's great to be a shareholder.
Learning
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Space Cadet
Regular
That settles it. Name is definitely PORN DOWN UNDER
That settles it. Name is definitely porn down under
SC
buena suerte :-)
BOB Bank of Brainchip
Do you think you will be a great addition to the already awesome Brainchip team Mr Lewis ?? Yes I do 'Rob'We will receive notification of any significant changes in his holdings in due course in the proscribed manner.
And, on another small matter, all of us want to hear from our new addition Dr. Tony (Ironman) Lewis but give the guy a break.
He hasn't even started yet and I'm sure has more important things to do during his on boarding and transition than feeding the chooks over here.
He still has to choose his secret laboratory colour scheme along with the ice bucket initiation and desk layout for all his cosplay and intern breaking activities.......
View attachment 50940
and don't forget we still have the commisioner PVDM looking over our shoulders keeping everything ship shape! 
Papacass
Regular
Well, in all the years I’ve been hanging around investment forums (25 or so) I’ve never made a prediction, mostly because I don’t have the rocks and also because I’m not a smart man haha. But…. here I go. I think the nexus between our world first tech, our meticulously built ecosystem, our partnerships, our JDAs, our IP licensees, our business model, our current management, our marketing and the current market opportunity for edge compute is about to bear fruit. It’s been a long road and I am now a design cycle expert and have had the patience of a monk. My prediction is in the next 6 months we will see our company grow and good revenue appear. Let’s see how this post ages.
PS Thank goodness sanity is prevailing and it is being acknowledged that AKD1000 is not a failure. AKD1000 is the acorn from where the mighty oak will sprout and is sprouting. I think both Sean and Antonio could have worded their thoughts on AKD1000 a lot better. I’m sure PVDM did not agree. Onward.
PS Thank goodness sanity is prevailing and it is being acknowledged that AKD1000 is not a failure. AKD1000 is the acorn from where the mighty oak will sprout and is sprouting. I think both Sean and Antonio could have worded their thoughts on AKD1000 a lot better. I’m sure PVDM did not agree. Onward.
Frangipani
Regular
Todd Vierra’s Impact Summit 2023 presentation on Sustainable Cities Using Smart Efficient AI is now online!
While the recording of that virtual workshop is publicly accessible via the Hackster.io Impact Summit 2023 website (https://events.hackster.io/impactsummit2023), a lot of the slides Todd showed are marked “2023-11 BrainChip Presentation Confidential”, so I assume we better not share them here? How about other slides from that same presentation that are not marked confidential?
And here are some more of Todd Vierra’s thoughts on Brainchip’s hyperefficient neuromorphic AI technology being critical for sustainable cities, accompanying the workshop presentation of his I had linked to earlier…
AI Puts the “Ability” in Sustainability
Discover how BrainChip's neuromorphic AI technology is enabling more resilient and sustainable cities.
www.hackster.io
AI Puts the “Ability” in Sustainability
Discover how BrainChip's neuromorphic AI technology is enabling more resilient and sustainable cities.
Sponsored by BrainChip
32 minutes ago • Machine Learning & AI
ByTodd Vierra
Sensors surround all who live in a modern city, capturing vibrations, air quality, water safety, energy usage, sights, sounds, and more. With AI, sensor-fed information is analyzed in real-time, feeding intelligent choices that enhance our quality of life.
BrainChip, an industry pioneer, enables deployment of AI “at the edge” — that is, in locations where the analysis and decisions occur at a sensor without connecting to the cloud or, in some cases, a power source. This truly unlocks the promise of portable, mobile AI. As demands for AI continue to grow, the compute and bandwidth required to support a cloud-only solution becomes economically prohibitive, making edge-based computing more critical than ever.
Neuromorphic AI is an emerging solution. that delivers extremely efficient processing by imitating the human brain. Neuromorphic solutions enable small footprint, low power, cost-effective and sustainable alternatives to traditional AI solutions.
How is neuromorphic AI critical for sustainable cities?
BrainChip has found methods to boost performance, accuracy, and efficiency by orders of magnitude, expanding AI’s edge use cases. This provides digital solutions within existing power/energy envelopes while amplifying the benefits of AI in delivering intelligent services which extend and optimize most aspects of urban life.Auto transportation
AI can optimize traffic flow, reduce congestion, and improve transportation efficiency through real-time data analysis and predictive modeling. Sensors throughout a smart city and a V2X framework can inform smart vehicles of alternate routes. BrainChip’s recent advances in quickly identifying objects (like pedestrians) through streaming video can drastically improve safety both through intelligent vehicular control as well as traffic management.Alternative energy
AI-driven systems can manage energy consumption in city infrastructure, optimizing lighting, heating, and cooling to reduce energy waste. Anomaly detection is used for predictive maintenance and system monitoring ensuring longer life of machinery, conserving resources, reducing downtime and reducing supply chain demands.New agriculture
Vertical hydroponic and oceanic farms help reduce pollution and improve air quality. These cannot scale without intelligent remote sensors and automation to maintain crops of the future and urban green spaces.Waste management
AI can optimize waste collection routes, predict when bins need emptying, and promote recycling and waste reduction initiatives. BrainChip’s collaboration with Circle8 Clean Technologies and AVID Group is entering the pilot phase in Australia to automate sorting and analyze data about consumer habits and the waste stream.Health
Portable medical devices, EEG, EKG analyzers that quickly assess health conditions; wearable or implantable devices monitoring and analyzing vital signs that identify known predictors of health issues, not only save lives and cost for individuals by enabling preventative care, but also assists health services readiness for patient arrival, improving outcomes.Public services
Improved Human Machine Interfaces (HMIs) with voice and vision combined with AI chatbots and virtual assistants can serve citizens to manage efficient interactions more intuitively. BrainChip's solutions have proven capability for accurate interpretation of voice commands, amongst other intelligence needed for this purpose.Connectivity
In high density urban conditions, last mile 5G infrastructure needs energy efficient, intelligent nodes for capacity and latency through base stations and Pico cell. Connectivity through satellites is also ramping up. Edge AI is becoming a critical component in all of them.Innovators are already applying neuromorphic AI and BrainChip is excited to partner with you to deliver on the promise and potential of this incredible technology. Share your ideas at sales@brainchip.com.
Todd Vierra is VP Customer Engagement for BrainChip, which delivers hyperefficient neuromorphic IP compute at the edge.
I’m sure this will be good for business!
ARLINGTON, VA – BlueHalo, a leading provider of critical capabilities and technologies across Space, Air, and Cyber domains, today announced it has acquired Ipsolon Research (“Ipsolon” or the “Company”). BlueHalo is a portfolio company of Arlington Capital Partners (“Arlington”), a Washington, D.C.-area private equity firm with extensive experience investing in regulated industries. Financial terms were not disclosed.
Founded in 2018, Ipsolon designs and manufactures high-performance, ultra-small form factor Software Defined Radios (“SDR”) for use in mission-critical spaces constrained by harsh environments. Ipsolon’s flagship SDR products include Cerberus, which offers leading functionalities and features in a small form factor that is made for movement on both land and air, and Chameleon, which provides substantial processing and multi-antenna capabilities in a single SDR module. Ipsolon’s defense-grade equipment, and specialization in ultra-small form factor SDRs, allows for a wide variety of use cases in military applications. Ipsolon’s reputation and ability to deliver rapid prototyping of SDR hardware and software for SDR applications has enabled the Company to deliver its critical solutions across a broad portfolio of demanding customers throughout the Department of Defense (“DoD”), including the United States Navy and United States Air Force.
The acquisition of Ipsolon directly complements several ongoing strategic and technological initiatives at BlueHalo and will allow for expedited timeframes for rapid prototyping and product delivery while ensuring quality across the supply chain. Ipsolon’s SDRs are already deployed within several BlueHalo products, and with Ipsolon now under BlueHalo’s roof the Company plans to further integrate Ipsolon’s SDRs across the product portfolio. Ipsolon’s proprietary hardware and software, coupled with BlueHalo’s existing technology, will allow the combined enterprise to deliver a superior suite of products to support the warfighter in the ever-evolving next generation battlefield.
“As SDRs continue to become more critical and ubiquitous in the development of next generation technology across the full portfolio of BlueHalo’s products, Ipsolon stood out as a perfect fit to further accelerate our mission and create efficiencies for our customers, deliver rapid capabilities, and facilitate increased creativity by having in-house SDR capabilities,” said Jonathan Moneymaker, Chief Executive Officer of BlueHalo. “We are incredibly excited to bring Ipsolon into BlueHalo and provide an integrated and superior set of solution offerings to our customers as we seek to continue innovating at mission speed.”
ARLINGTON, VA – BlueHalo, a leading provider of critical capabilities and technologies across Space, Air, and Cyber domains, today announced it has acquired Ipsolon Research (“Ipsolon” or the “Company”). BlueHalo is a portfolio company of Arlington Capital Partners (“Arlington”), a Washington, D.C.-area private equity firm with extensive experience investing in regulated industries. Financial terms were not disclosed.
Founded in 2018, Ipsolon designs and manufactures high-performance, ultra-small form factor Software Defined Radios (“SDR”) for use in mission-critical spaces constrained by harsh environments. Ipsolon’s flagship SDR products include Cerberus, which offers leading functionalities and features in a small form factor that is made for movement on both land and air, and Chameleon, which provides substantial processing and multi-antenna capabilities in a single SDR module. Ipsolon’s defense-grade equipment, and specialization in ultra-small form factor SDRs, allows for a wide variety of use cases in military applications. Ipsolon’s reputation and ability to deliver rapid prototyping of SDR hardware and software for SDR applications has enabled the Company to deliver its critical solutions across a broad portfolio of demanding customers throughout the Department of Defense (“DoD”), including the United States Navy and United States Air Force.
The acquisition of Ipsolon directly complements several ongoing strategic and technological initiatives at BlueHalo and will allow for expedited timeframes for rapid prototyping and product delivery while ensuring quality across the supply chain. Ipsolon’s SDRs are already deployed within several BlueHalo products, and with Ipsolon now under BlueHalo’s roof the Company plans to further integrate Ipsolon’s SDRs across the product portfolio. Ipsolon’s proprietary hardware and software, coupled with BlueHalo’s existing technology, will allow the combined enterprise to deliver a superior suite of products to support the warfighter in the ever-evolving next generation battlefield.
“As SDRs continue to become more critical and ubiquitous in the development of next generation technology across the full portfolio of BlueHalo’s products, Ipsolon stood out as a perfect fit to further accelerate our mission and create efficiencies for our customers, deliver rapid capabilities, and facilitate increased creativity by having in-house SDR capabilities,” said Jonathan Moneymaker, Chief Executive Officer of BlueHalo. “We are incredibly excited to bring Ipsolon into BlueHalo and provide an integrated and superior set of solution offerings to our customers as we seek to continue innovating at mission speed.”
Frangipani
Regular
I should really go to bed soon, but I just keep on finding Brainchip-related nuggets online today - believe it or not, even a job ad from Ukraine 
!
Junior Machine Learning Engineer at Data Science UA
**About us:**<br>Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of Kyiv’s first technical Data Science UA Conference,...
Junior Machine Learning Engineer
Data Science UA
Diana Marchenko, IT Recruiter
About us:
We are Data Science UA, and we are a fast-growing IT service company. We are proud of developing the Data Science community in Ukraine for more than 7 years. Data Science UA unites all researchers, engineers, and developers around Data Science and related areas. We conduct events on machine learning, computer vision, intelligence, information science, and the use of artificial intelligence for business in various fields.
About role:
Data Science UA is looking for a Junior Machine Learning Engineer to become a helping hand for our internal Data Science team. Do you want to work on some real projects for our Clients and/or perform R&D of novel AI algorithms and platforms (in particular, on the neuromorphic platform Brainchip Akida)? Then apply and join our team! You will report directly to our Head of AI Consulting, PhD, which is a great opportunity for you and your further growth.
Requirements:
0,5-1 year of experience as ML Engineer/Data Scientist or related (alternatively, participation in open-source ML projects or Kaggle competitions);Minimal expertise in CV or NLP-based projects;Good knowledge of math, CS and AI fundamentals;Proficiency in Python;Student/graduate in the field of exact sciences (computer science, mathematics, cybernetics, physics, etc.);Intermediate English.Nice to have:
✔Have your own pet projects;
✔Completed different Data Science related courses.
Responsibilities:
Participation in AI consulting projects for Clients from Ukraine and abroad;Work on complex R&D projects;Write and publish scientific papers (and possibly your diploma in University);Preparation of technical content (presentations, analytical articles).We offer:
Opportunity to grow and participate in large projects of our AI R&D centers;Possibility of remote work;Development of professional skills and support in acquiring new knowledge (by attending conferences on Data Science, exchange of experience, etc.);Friendly team;Interesting tasks.About Data Science UA
We understand that you need more than just a search engine to find IT and technical professionals or to progress your own career, that is why we established Data Scientist UA.One place connecting business and developers.
Company website:
https://data-science-ua.com/
DOU company page:
https://jobs.dou.ua/companies/data-science-ua/
Job posted on 29 November 2023
50 views 15 applications
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buena suerte :-)
BOB Bank of Brainchip
Where are you Frangi?I should really go to bed soon, but I just keep on finding Brainchip-related nuggets online today - believe it or not, even a job ad from Ukraine
Junior Machine Learning Engineer
Data Science UA
Diana Marchenko, IT Recruiter
About us:
We are Data Science UA, and we are a fast-growing IT service company. We are proud of developing the Data Science community in Ukraine for more than 7 years. Data Science UA unites all researchers, engineers, and developers around Data Science and related areas. We conduct events on machine learning, computer vision, intelligence, information science, and the use of artificial intelligence for business in various fields.
About role:
Data Science UA is looking for a Junior Machine Learning Engineer to become a helping hand for our internal Data Science team. Do you want to work on some real projects for our Clients and/or perform R&D of novel AI algorithms and platforms (in particular, on the neuromorphic platform Brainchip Akida)? Then apply and join our team! You will report directly to our Head of AI Consulting, PhD, which is a great opportunity for you and your further growth.
Requirements:
Nice to have:
✔Have your own pet projects;
✔Completed different Data Science related courses.
Responsibilities:
We offer:
About Data Science UA
We understand that you need more than just a search engine to find IT and technical professionals or to progress your own career, that is why we established Data Scientist UA.
One place connecting business and developers.
Company website:
https://data-science-ua.com/
DOU company page:
https://jobs.dou.ua/companies/data-science-ua/
Job posted on 29 November 2023
50 views 15 applications
Frangipani
Regular
In the Land of Poets and Thinkers…
buena suerte :-)
BOB Bank of Brainchip
...... Gute NachtAgree. Remember, with at least one customer, bringing out Akida 2 trumped our own ace.
db1969oz
Regular
ASX releases the number of shorts takin out yesterday at approximately 11am. I don’t know if there is anyway possible to find out todays?Good morning everyone! What do shorts look like today? Does anyone have an account with access to that? Thank you all!
buena suerte :-)
BOB Bank of Brainchip
ndefries
Regular
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