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PhD Position in Clinical data science, Machine learning, Computer security - SDU, Denmark

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We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development of complex compute systems. The applicant should have provable skills in the state-of-the-art web-development frameworks, virtualization techniques as well as database technologies. Expertise in clinical data science and machine learning, as well as computer security and data privacy are welcome. A large roadblock of medical research is the difficult access to sensitive data which therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital.


Computer vision-based anomaly detection using Amazon Lookout for Vision and AWS Panorama

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This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment. Operational excellence is a key priority at Tyson Foods. Predictive maintenance is an essential asset for achieving this objective by continuously improving overall equipment effectiveness (OEE).


Effective technology education driven through artificial intelligence

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Artificial Intelligence is the process of making use of computers and machines to mimic human perception, decision-making, and other processes to complete a task. Put in other words, AI is


Top Data Science & AI Trends For 2022

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Its adoption accelerated, and leaders correctly predicted growth in the industry in all aspects. Overall, organisations invested more in Data Science, and there was an upswing in the Data Science jobs. While the median salaries of analytics professionals saw a slight decline at the start of the year, a rising trend was witnessed again in the recent months, which will continue to be the case in the coming year. The inefficiencies of Data Science teams from development to deployment in the real world were observed before but they became even more evident due to the pandemic. The operationalisation and scaling of Machine Learning models through structured frameworks was the talk of 2021. These processes will start getting streamlined in the coming years. The Data Science industry also realised the breadth of roles needed for these deployments. While generalists will continue to be in demand, niche roles will play an important role going forward, especially Data Engineers. Subsequently, the role of education will also evolve. It will become further formalised with more specialisation courses introduced.


Digital health and data science: New component of medical education curriculum introduced

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The Augusta Webster, MD, Office of Medical Education (AWOME) has begun introducing a new component of the medical education curriculum to current medical students: instruction in Digital Health and Data Science. The curriculum is being co-developed by David Liebovitz, MD, associate vice chair for clinical informatics in the Department of Medicine and co-director of the Center for Medical Education in Data Science and Digital Health, and Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology. The utilization of large data sets and machine learning is rapidly growing in healthcare. Feinberg is proud to be at the forefront of preparing our students to not only utilize this technology in care delivery and research, but also to critically evaluate its applicability and limitations. I am confident that this curriculum will be the foundation for many of our students to become leaders in the field of data science and augmented intelligence in medicine." The new curriculum component will see students meeting several core competencies and learning outcomes while learning about the health data ecosystem; the health IT regulatory environment; data science methods and research; digital health decision support; bias, ethics and health equity; and the sociotechnical context for digital health and data science. Mahesh Vaidyanathan, MD, MBA, assistant professor of Anesthesiology, is a co-leader of Feinberg's new Digital Health and Data Science curriculum component for medical students. "The tools that data science brings to clinical care enable more effective and personalized care for our patients.


AI vs Machine Learning vs Deep Learning

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Let me tell you a story, before I get into the topic -- I am a Computer Engineering Student and it was my first year of college. And, Everyone was suggesting me to study and specialize about "AI and Machine Learning(ML)" because they say it is a high demand and a high-paying job. Of course, I agree with their ideas and the reasons. But, whenever I asked: "What is AI or ML?" Mostly everyone said to me -- Its the same i.e. teaching computers to behave like a human. My point is: Most people don't know and they are confused about, what is the small difference between AI, Machine Learning and Deep Learning?


Meta claims its AI improves speech recognition quality by reading lips

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People perceive speech both by listening to it and watching the lip movements of speakers. In fact, studies show that visual cues play a key role in language learning. By contrast, AI speech recognition systems are built mostly -- or entirely -- on audio. And they require a substantial amount of data to train, typically ranging in the tens of thousands of hours of recordings. To investigate whether visuals -- specifically footage of mouth movement -- can improve the performance of speech recognition systems, researchers at Meta (formerly Facebook) developed Audio-Visual Hidden Unit BERT (AV-HuBERT), a framework that learns to understand speech by both watching and hearing people speak.


Senior Machine Learning / Computer Vision Engineer

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Glass Imaging is looking for a Senior Deep Learning / Computer Vision Algorithms Engineer, to work on advanced problems in computational photography. You would be responsible for bringing the latest cutting edge Computer Vision models into production on embedded devices and smartphones, from investigating, developing and training Deep Learning models and algorithms, to optimizing them for high throughput real time imaging applications. Founded by former Apple Engineers who brought you Portrait Mode and other iPhone camera features, Glass is building the future of miniaturized imaging that delivers astonishing image quality and user experience. You'd be joining a unique team of creative and enthusiastic engineers with a passion and track record for revolutionizing the world of photography. We are a funded early-stage startup with great benefits (including stock options, competitive pay and health insurance), a small (but growing) and friendly team.


Artificial Intelligence Degrees

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Artificial intelligence (AI) is relatively new degree subject. With the rise of artificial intelligence, and the prominence of new technology, education is becoming increasingly important. A degree in artificial intelligence would allow you to be prepared to work in an area of computer science that is only going to become more utilised and improved. It is common for artificial intelligence to offered as a joint honours degree alongside a more generic computer science degree. This format ensures that you have a foundation of computer science knowledge, as well as having specialist knowledge in artificial intelligence. You will study modules in computer programming, software engineering, artificial intelligence and machine learning.


Stop Learning Data Science to Find Purpose and Find Purpose to Learn Data Science - KDnuggets

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Data scientists are in demand, there are no two ways about it. The jobs pay well, there are plenty of openings available, and the industry only appears to be growing in this post-pandemic digital world. It should come as no surprise then that data science students are also a growing sector of the world labor force. But learning data science is not easy. I remember my own experience trying to go from a data-savvy academic researcher to an industry data science professional.