Despite Cardano (ADA) taking a cue from Bitcoin (BTC) and the rest of the crypto sector in recent sluggishness, a deep learning algorithm has predicted it still has enough room to recover, perhaps even hitting the price of $0.50 by September 1, 2023.

Indeed, NeuralProphets PyTorch-based prediction algorithm that relies on an open-source machine learning framework has projected that ADA would hit $0.51 in the next month, an increase of 73.4% from its current price, as per the most recent data seen by Finbold on August 4.

Although the above model, which covers the period between January 1 and December 31, 2023, is not an accurate indicator of future prices and should not be taken as such, its predictions have historically proven to be relatively correct.

At the same time, the advanced machine learning algorithms deployed by the cryptocurrency analytics and forecasting platform PricePredictions are more bearish, having set the price of Cardano on September 1, 2023, at $0.275974. according to the latest information.

As things stand, Cardano is currently changing hands at the price of $0.29429, which is an advance of 0.09% in the last 24 hours, a decline of 5.59% across the previous seven days, and a 2.75% gain over the past month, as the charts show.

Meanwhile, the Cardano blockchain development team has continued to make strides, including with the recent launch of Mithril, a stake-based signature protocol to improve the efficiency of the node sync, and its founder Charles Hoskinson debunking the ghost chain myth, all of which could contribute to ADAs price.

Disclaimer: The content on this site should not be considered investment advice. Investing is speculative. When investing, your capital is at risk.

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Deep learning (DL), a branch of artificial intelligence (AI), has made significant strides in the medical field. It utilizes artificial neural networks (ANN) to learn from large amounts of data and extract relevant information for various tasks. DL has found applications in imaging diagnosis, clinical and drug research, disease classification and prediction, personalized therapy design, and public health monitoring. The advantages of DL over traditional data analysis methods include improved performance and automation. It also provides evidence-based clinical decision support tools to healthcare professionals.

However, DL presents challenges and limitations. One challenge is the need for quality and representative data. ANNs can fail to generalize when trained on data that does not accurately reflect the problem being addressed. In the medical field, privacy laws like the General Data Protection Regulation (GDPR) restrict the use of clinical data without patient consent. Even with consent, data must be anonymized and ethical approval obtained before use.

Federated learning (FL) offers a solution to these challenges. FL is a privacy-preserving and GDPR-compliant strategy for distributed machine learning. It allows a federation of clients to learn a model without exchanging data. This enables the utilization of vast and diverse medical data available from different sources, increasing the statistical power and generalizability of ML models while addressing privacy, security, and data governance concerns. FL has been successfully applied in various clinical fields, including imaging diagnosis, drug research, and genomics.

Although FL enables data sharing, the lack of explainability in ML models, like ANNs, is a limitation. Explainable AI (XAI) solutions provide tools to interpret and understand ML algorithms. Data type-specific solutions, such as Grad-CAM for image classification, and data type-independent solutions like LIME or NAMs, can be used to enhance interpretability.

Making ML models interpretable is a step towards Trustworthy AI, which ensures reliability and ethicality. XAI helps build robust and ethically sound AI systems.

The CADUCEO project, focused on digestive system diseases, proposes a federated platform that employs FL algorithms. This platform allows medical centers to share knowledge without compromising patient privacy. The project also introduces machine learning algorithms for automated image processing, data augmentation, and diagnosis support.

In conclusion, DL has the potential to improve medical operations in terms of efficiency and treatment quality. With FL and XAI, the challenges associated with data sharing and model interpretability can be addressed, leading to advancements in medical AI applications.

Note: The rest of the article includes details on the materials and methods used, results, functionalities, use cases, and future work.

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Revolutionizing Telecommunications: The Impact of Deep Learning on Global Connectivity

The telecommunications industry is on the brink of a significant transformation, thanks to the advent of deep learning technologies. Deep learning, a subset of artificial intelligence (AI), is poised to revolutionize global connectivity, bringing about unprecedented changes in the way we communicate and interact with the world.

Deep learning algorithms, which mimic the human brains ability to learn from experience, are being harnessed to improve the efficiency, reliability, and security of telecommunications networks. These algorithms can analyze vast amounts of data, identify patterns, and make predictions, enabling telecom companies to optimize network performance, predict and prevent outages, and enhance customer experience.

One of the most significant impacts of deep learning on telecommunications is in the area of network optimization. Telecom networks generate massive amounts of data every second. Analyzing this data manually to optimize network performance is virtually impossible. However, deep learning algorithms can sift through this data, identify patterns, and make predictions about network performance. This allows telecom companies to proactively address issues, optimize bandwidth allocation, and ensure seamless connectivity for their customers.

Moreover, deep learning is playing a crucial role in enhancing the security of telecommunications networks. Cybersecurity threats are a significant concern for telecom companies, given the sensitive nature of the data they handle. Deep learning algorithms can analyze network traffic, identify unusual patterns, and flag potential security threats. This proactive approach to cybersecurity can help prevent data breaches and protect customer information.

In addition to network optimization and security, deep learning is also transforming customer experience in the telecom sector. Telecom companies are using deep learning algorithms to analyze customer behavior, predict their needs, and personalize their services. This not only enhances customer satisfaction but also helps telecom companies retain their customers and increase their market share.

Furthermore, deep learning is paving the way for the development of advanced telecommunications technologies. For instance, it is playing a crucial role in the development of 5G technology, which promises to revolutionize global connectivity with its high-speed, low-latency connectivity. Deep learning algorithms are being used to optimize the allocation of 5G spectrum, enhance network performance, and ensure seamless connectivity.

However, the integration of deep learning into telecommunications is not without its challenges. Telecom companies need to invest in advanced infrastructure and skilled personnel to harness the power of deep learning. They also need to address concerns related to data privacy and security, given the sensitive nature of the data they handle.

Despite these challenges, the potential benefits of integrating deep learning into telecommunications are immense. It promises to revolutionize global connectivity, enhance customer experience, and pave the way for the development of advanced telecommunications technologies. As such, telecom companies around the world are investing heavily in deep learning, heralding a new era in global connectivity.

In conclusion, deep learning is set to revolutionize the telecommunications industry. Its ability to analyze vast amounts of data, identify patterns, and make predictions can help telecom companies optimize network performance, enhance security, and improve customer experience. While there are challenges to overcome, the potential benefits of integrating deep learning into telecommunications are immense. As we move towards a more connected world, deep learning will play a crucial role in shaping the future of telecommunications.

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Deep learning is an advanced type of artificial intelligence that uses neural networks and complex algorithms to process big data and produce detailed and contextualized outputs, simulating the ways in which human brains process and share information.

This type of artificial intelligence is the foundation for a number of emerging technologies, but despite its many advantages, it also brings forth distinct disadvantages that users need to be aware of.

A quick summary: There are both pros and cons to the practice of deep learning. As far as pros go:users can benefit from a machine learning solution that is highly scalable, automated, hands-off, and capable of producing state-of-the-art AI models, such as large language models. However, the cons are also significant: Deep learning is expensive, consumes massive amounts of power, and creates both ethical and security concerns through its lack of transparency.

Deep learning is a type of artificial intelligence that consists of neural networks with multiple layers, algorithmic training that teaches these neural networks to mimic human brain activity, and training datasets that are massive and nuanced enough to address various AI use cases. Deep learning uses large language models.

Because of its complex neural network architecture, deep learning is a mature form of artificial intelligence that can handle higher-level computation tasks, such as natural language processing, fraud detection, autonomous vehicle driving, and image recognition. Deep learning is one of the core engines running at the heart of generative AI technology.

Examples of deep learning models and their neural networks include the following:

Also see:Generative AI Companies: Top 12 Leaders

Deep learning is a specialized type of machine learning. It has more power and can handle large amounts of different types of data, whereas a typical machine learning model operates on more general tasks and a smaller scale.

Deep learning is primarily used for more complex projects that require human-level reasoning, like designing an automated chatbot or generating synthetic data, for example.

Learn more: Machine Learning vs. Deep Learning

Neural networks constitute a key piece of deep learning model algorithms, creating the human-brain-like neuron pattern that supports deep model training and understanding. A single-layer neural network is whats used in most traditional AI/ML models, but with deep learning models, multiple neural networks are present. A model is not a deep learning model unless it has at least three neural networks, but many deep learning models have dozens of neural networks.

Also see:Best Artificial Intelligence Software 2023

Deep learning models are designed to handle various inputs and learn through different methods. Many businesses choose to use deep learning models because they can learn and act on tasks independent of hands-on human intervention and data labeling. Their varied learning capabilities also make them great AI models for scalable automation.

Although there are subsets and nuances to each of these learning types, deep learning models can learn through each of the following methods:

Generative AI models are the latest and greatest in the world of artificial intelligence, giving businesses and individuals alike the opportunity to generate original content at scale, usually from natural language inputs.

But these models can only produce logical responses to user queries because of the deep learning and neural network mechanisms that lie at their foundation, allowing them to generate reasonable and contextualized responses on a grand scale and about a variety of topics.

More on this topic: Top 9 Generative AI Applications and Tools

Unstructured datasets especially large unstructured datasets are difficult for most artificial intelligence models to interpret and apply to their training. That means that, in most cases, images, audio, and other types of unstructured data either need to go through extensive labeling and data preparation to be useful, or do not get used at all in training sets.

With deep learning neural networks, unstructured data can be understood and applied to model training without any additional preparation or restructuring. As deep learning models have continued to mature, a number of these solutions have become multimodal and can now accept both structured written content and unstructured image inputs from users.

The neural network design of deep learning models is significant because it gives them the ability to mirror even the most complex forms of human thought and decision-making.

With this design, deep learning models can understand the connections between and the relevance of different data patterns and relationships in their training datasets. This human-like understanding can be used for classification, summarization, quick search and retrieval, contextualized outputs, and more without requiring the model to receive guided training from a human.

Because deep learning models are meant to mimic the human brain and how it operates, these AI models are incredibly adaptable and great multitaskers. This means they can be trained to do more and different types of tasks over time, including complex computations that normal machine learning models cant do and parallel processing tasks.

Through strategies like transfer learning and fine-tuning, a foundational deep learning model can be continually trained and retrained to take on a variety of business and personal use cases and tasks.

Deep learning models require more computing power than traditional machine learning models, which can be incredibly costly and require more hardware and compute resources to operate. These computing power requirements not only limit accessibility but also have severe environmental consequences.

Take generative AI models, for example: Many of these deep learning models have not yet had their carbon footprint tested, but early research about this type of technology suggests that generative AI model emissions are more impactful than many roundtrip airplane fights. While not all deep learning models require the same amount of energy and resources that generative AI models do, they still need more than the average AI tool to perform their complex tasks.

Deep learning models are typically powered with graphics processing units (GPUs), specialized chips, and other infrastructure components that can be quite expensive, especially at the scale that more advanced deep learning models require.

Because of the quantity of hardware these models need to operate, theres been a GPU shortage for several years, though some experts believe this shortage is coming to an end. Additionally, only a handful of companies make this kind of infrastructure. Without the right quantity and types of infrastructure components, deep learning models cannot run.

Data scientists and AI specialists more than likely know whats in the training data for deep learning models. However, especially for models that learn through unsupervised learning, these experts may not fully understand the outputs that come out of these models or the processes deep learning models follow to get those results.

As a consequence, users of deep learning models have even less transparency and understanding of how these models work and deliver their responses, making it difficult for anyone to do true quality assurance.

Even though deep learning models can work with data in varying formats, both unstructured and structured, these models are only as good as the data and training they receive.

Training and datasets need to be unbiased, datasets need to be large and varied, and raw data cant contain errors. Any erroneous training data, regardless of how small the error, could be magnified and made worse as models are fine-tuned and scaled.

Deep learning models have introduced a number of security and ethical concerns into the AI world. They offer limited visibility into their training practices and data sources, which opens up the possibility of personal data and proprietary business data getting into training sets without permission.

Unauthorized users could get access to highly sensitive data, leading to cybersecurity issues and other ethical use concerns.

More on a similar topic: Generative AI Ethics: Concerns and Solutions

Deep learning is a powerful artificial intelligence tool that requires dedicated resources and raises some significant concerns. However, the pros outweigh the cons at this point, as deep learning gives businesses the technology backbone they need to develop and run breakthrough solutions for everything from new pharmaceuticals to smart city infrastructure.

The best path forward is not to get rid of or limit deep learnings capabilities but rather to develop policies and best practices for using this technology in a responsible way.

Read next: 100+ Top Artificial Intelligence (AI) Companies

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The Pros and Cons of Deep Learning | eWeek - eWeek

Exploring the Potential of AI EfficientNet: Breakthroughs in Deep Learning and Computer Vision

Artificial intelligence (AI) has come a long way in recent years, with advancements in deep learning and computer vision leading the charge. One of the most promising developments in this field is the AI EfficientNet, a family of advanced deep learning models that have the potential to revolutionize various industries and applications. In this article, we will explore the potential of AI EfficientNet and discuss some of the breakthroughs it has made in deep learning and computer vision.

Deep learning, a subset of machine learning, involves training artificial neural networks to recognize patterns and make decisions based on large amounts of data. One of the most significant challenges in deep learning is creating models that are both accurate and efficient. This is where AI EfficientNet comes in. Developed by researchers at Google AI, EfficientNet is a family of models that are designed to be both highly accurate and computationally efficient. This is achieved through a technique called compound scaling, which involves scaling the depth, width, and resolution of the neural network simultaneously.

The development of AI EfficientNet has led to several breakthroughs in deep learning and computer vision. One of the most notable achievements is the improvement in image classification accuracy. EfficientNet models have been able to achieve state-of-the-art accuracy on the ImageNet dataset, a widely used benchmark for image classification algorithms. This is particularly impressive considering that EfficientNet models are significantly smaller and faster than other leading models, making them more suitable for deployment on devices with limited computational resources, such as smartphones and IoT devices.

Another significant breakthrough made possible by AI EfficientNet is the improvement in object detection and segmentation. These tasks involve identifying and locating objects within an image and are crucial for applications such as autonomous vehicles, robotics, and surveillance systems. EfficientNet models have been combined with other deep learning techniques, such as the Focal Loss and the Feature Pyramid Network, to create state-of-the-art object detection and segmentation systems. These systems have achieved top performance on benchmark datasets such as COCO and PASCAL VOC, demonstrating the potential of AI EfficientNet in these critical applications.

The advancements made by AI EfficientNet in deep learning and computer vision have far-reaching implications for various industries and applications. In healthcare, for example, EfficientNet models can be used to improve the accuracy of medical image analysis, enabling faster and more accurate diagnosis of diseases. In agriculture, these models can be used to analyze satellite imagery and identify areas that require attention, such as regions affected by pests or diseases. In retail, AI EfficientNet can be used to improve the accuracy of visual search engines, making it easier for customers to find the products they are looking for.

Furthermore, the efficiency of AI EfficientNet models makes them ideal for deployment on edge devices, such as smartphones, drones, and IoT devices. This opens up new possibilities for real-time applications, such as facial recognition, object tracking, and augmented reality. By bringing advanced deep learning capabilities to these devices, AI EfficientNet has the potential to transform the way we interact with technology and the world around us.

In conclusion, AI EfficientNet represents a significant breakthrough in deep learning and computer vision, offering state-of-the-art accuracy and efficiency in a range of applications. From healthcare to agriculture, retail to edge devices, the potential of AI EfficientNet is vast and exciting. As researchers continue to refine and expand upon this technology, we can expect to see even more impressive advancements in the field of artificial intelligence, ultimately leading to a more connected, intelligent, and efficient world.

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Exploring the Synergy between AI Deep Learning and Quantum Computing: Unleashing New Possibilities

The intersection of artificial intelligence (AI) deep learning and quantum computing is creating a powerful partnership that promises to revolutionize the way we solve complex problems and transform industries. As we continue to explore the synergy between these two cutting-edge technologies, we are witnessing the emergence of new possibilities and applications that were once considered science fiction.

AI deep learning, a subset of machine learning, involves the use of artificial neural networks to enable machines to learn and make decisions without explicit programming. This technology has already made significant strides in areas such as image and speech recognition, natural language processing, and autonomous vehicles. However, the computational power required to process and analyze the vast amounts of data involved in deep learning is immense, and this is where quantum computing comes into play.

Quantum computing, which leverages the principles of quantum mechanics, has the potential to solve problems that are currently intractable for classical computers. Unlike classical computers that use bits to represent information as 0s and 1s, quantum computers use quantum bits, or qubits, which can represent both 0 and 1 simultaneously. This allows quantum computers to perform multiple calculations at once, exponentially increasing their processing power.

The convergence of AI deep learning and quantum computing is expected to unlock new possibilities in various fields. For instance, in drug discovery, quantum computing can be used to simulate and analyze complex molecular structures, while AI deep learning can help identify patterns and predict the effectiveness of potential treatments. This powerful combination could significantly accelerate the drug discovery process, ultimately leading to more effective treatments for a wide range of diseases.

In the field of finance, quantum computing can optimize trading strategies and risk management, while AI deep learning can analyze large datasets to predict market trends and identify investment opportunities. Together, these technologies could revolutionize the financial industry by providing more accurate predictions and enabling faster, more informed decision-making.

Moreover, the partnership between AI deep learning and quantum computing has the potential to enhance cybersecurity. Quantum computers can efficiently solve complex cryptographic problems, while AI deep learning can detect and respond to cyber threats in real-time. This combination could lead to the development of more secure communication systems and robust defense mechanisms against cyberattacks.

However, the integration of AI deep learning and quantum computing is not without its challenges. One of the main hurdles is the current lack of mature quantum hardware, as quantum computers are still in their infancy and not yet capable of outperforming classical computers in most tasks. Additionally, developing algorithms that can harness the full potential of quantum computing for AI deep learning is a complex task that requires a deep understanding of both fields.

Despite these challenges, researchers and tech giants such as Google, IBM, and Microsoft are investing heavily in the development of quantum computing and AI deep learning technologies. As these efforts continue, we can expect to see significant advancements in the coming years that will further strengthen the partnership between AI deep learning and quantum computing.

In conclusion, the intersection of AI deep learning and quantum computing holds immense promise for solving complex problems and transforming industries. By harnessing the power of these two cutting-edge technologies, we can unlock new possibilities and applications that will shape the future of technology and innovation. As we continue to explore the synergy between AI deep learning and quantum computing, we are poised to witness a technological revolution that will redefine the boundaries of what is possible.

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A research team from the Universidad Carlos III de Madrid (UC3M), together with University College London in the United Kingdom, has carried out a study to analyze how chronic pain affects each patient's body.Within this framework, a deep learning method has been developed to analyze the biometric data of people with chronic conditions.

The analysis is based on the hypothesis that people with chronic lower back pain have variations in their biometric data compared to healthy people.These variations are related to body movements or walking patterns and are believed to be due to an adaptive response to avoid further pain or injury.

However, research to date has found it difficult to accurately distinguish these biometric differences between people with and without pain.There have been several factors, such as the scarcity of data related to this issue, the particularities of each type of chronic pain and the inherent complexity in the measurement of biometric variables.

People with chronic pain often adapt their movements to protect themselves from further pain or injury.This adaptation makes it difficult for conventional biometric analysis methods to accurately capture physiological changes.Hence the need to develop this system, says Doctor Mohammad Mahdi Dehshibi, a postdoctoral researcher at the i_mBODY Laboratory in UC3M's Computer Science Department, who led this study.

The research carried out by UC3M has developed a new method that uses a type of deep learning called s-RNNs (sparsely connected recurrent neural networks) together with GRUs (closed recurrent units), which are a type of neural network unit that is used to model sequential data.With this development, the team has managed to capture changes in pain-related body behavior over time.Furthermore, it surpasses existing approaches to accurately classify pain levels and pain-related behavior.

The innovation of the proposed method has been to take advantage of an advanced deep learning architecture and add additional features to address the complexities of sequential data modelling.The ultimate goal is to achieve more robust and accurate results related to sequential data analysis.

One of the main research focuses in our lab is the integration of deep learning techniques to develop objective measures that improve our understanding of people's body perceptions through the analysis of body sensor data, without relying exclusively on direct questions to individuals, says Ana Tajadura Jimnez, a lecturer from UC3M's Computer Science Department and lead researcher of the BODYinTRANSIT project, who leads the i_mBODY Laboratory.

The new method developed by the UC3M research team has been tested with the EmoPain database, which contains data on pain levels and behaviors related to these levels.This study also highlights the need for a reference database dedicated to analyzing the relationship between chronic pain and biometrics.This database could be used to develop applications in areas such as security or healthcare, says Mohammad Mahdi.

These results of this research are used in the design of new medical therapies focused on the body and different clinical conditions.In healthcare, the method can be used to improve the measurement and treatment of chronic pain in people with conditions such as fibromyalgia, arthritis and neuropathic pain.It can help control pain-related behaviors and tailor treatments to improve patient outcomes.In addition, it can be beneficial for monitoring pain responses during post-surgical recovery, says Mohammad Mahdi.

In this regard, Ana Tajadura also highlights the relevance of this research for other medical processes: In addition to chronic pain, altered movement patterns and negative body perceptions have been observed, such as in eating disorders, chronic cardiovascular disease or depression, among others .It is extremely interesting to carry out studies using the above method in these populations in order to better understand medical conditions and their impact on movement.These studies could provide valuable information for the development of more effective screening tools and treatments, and improve the quality of life of people affected by these conditions.

In addition to health applications, the results of this project can be used for the design of sports, virtual reality, robotics or fashion and art applications, among others.

This research is carried out within the framework of the BODYinTRANSIT project, led by Ana Tajadura Jimnez and funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (GA 101002711).

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Deep learning method developed to understand how chronic pain ... - EurekAlert

Researchers at the University of California, Los Angeles have conducted a study to test the cognitive abilities of deep learning models. Using the GPT-3 large language model, they found that it performed at or above human capabilities for resolving complex reasoning problems. Specifically, the researchers tested the model on analogical tasks, such as the Ravens Progressive Matrices, which require test takers to identify patterns.

The results showed that the AI performed at the higher end of humans scores and made a few of the same mistakes. The researchers also asked the AI to solve a set of SAT analogy questions involving word pairs, in which it performed slightly above the average human level. However, the AI struggled with analogy problems based on short stories.

The study suggested that the AI could be employing a mapping process similar to how humans approach these types of problems. The researchers speculated that the AI might have developed some alternate form of machine intelligence.

It is important to note that the AIs performance was based on its training data, which has not been publicly disclosed by OpenAI, the creator of GPT-3. Therefore, it is unclear whether the AI is genuinely reasoning or if it is simply relying on its training data to generate answers.

Overall, this study adds to the ongoing discussion about the cognitive abilities of AI systems. While the AI showed promise in certain areas, there are still limitations and questions about its true intelligence. Further research is needed to understand the capabilities and limitations of deep learning models.

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The Cognitive Abilities of Deep Learning Models - Fagen wasanni

School of Physics, Mathematics and Computing Department of Computer Science and Software Engineering

The University of Western Australia (UWA) is ranked among the top 100 universities in the world and a member of the prestigious Australian Group of Eight research intensive universities. With a strong research track record, vibrant campus and working environments, supported by the freedom to innovate and inspire, there is no better time to join Western Australias top university.

About the team

The Department of Computer Science and Software Engineering under the School of Physics, Mathematics and Computing is renowned for its award-winning researchers, teachers and facilities. The broad-based undergraduate and postgraduate programs are complemented by a wide range of research activities and the School is a leader in developing graduates with high level expertise in computer programming and the methods involved in performing complex computations and processing data. In the resource rich state of Western Australia the opportunities for partnership and collaborative research are extensive and the School has well established links with industry.

About the opportunity

As the appointee, you will primarily be involved in the development of state-of-the-art computer vision and deep learning algorithms, with a focus on object detection. The scope of this research has broad applicability, including but not limited to domains such as ecology, agriculture, augmented reality, and surveillance. As a key member of our multidisciplinary team, you will contribute to ground-breaking research, creating cutting-edge solutions that have real-world applications. This opportunity will provide you with a platform to leverage your skills and expertise to shape the future of these fields, and also a unique chance to collaborate with other brilliant minds.

About you

You will be an ambitious individual looking to push the boundaries of technology and make significant contributions to the field. This opportunity will provide you with a platform to leverage your skills and expertise to shape the future of these fields, and also a unique chance to collaborate with other brilliant minds.

To be considered for this role, you will demonstrate:

About your application

Full details of the position's responsibilities and the selection criteria are outlined in the position description: PD - Research Fellow - 51531.pdf

The content of your Resume and Cover Letter should demonstrate how you meet the selection criteria.

Closing date: 11:55 PM AWST on Sunday, 13 August 2023

To learn more about this opportunity, please contact Professor Mohammed Bennamoun at mohammed.bennamoun@uwa.edu.au) and Professor Farid Boussaid at farid.boussaid@uwa.edu.au

This position is only open to applicants with relevant rights to work in Australia.

Application Details: Please apply online via the Apply Now button.

Our commitment to inclusion and diversity

UWA is committed to a diverse workforce and an equitable and inclusive workplace. We celebrate difference and believe diversity is fundamental to achieving our goals as a globally recognised Top 100 educational and research institution. We are committed to creating a safe work environment for Aboriginal and Torres Strait Islander people, women, people from culturally and linguistically diverse backgrounds, the LGBTIQA+ community and people living with disability.

Should you have any queries relating to your application, please contact the individual named in the advertisement. Alternatively, contact the Talent team at talent-hr@uwa.edu.au with details of your query. To enable a quick response, please include the 6-digit job reference number and a member of the team will respond to your enquiry.

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Research Fellow: Computer Vision and Deep Learning job with ... - Times Higher Education