Mediaboss Marketing

Job Description

Vessel Collision Avoidance System is a real-time framework to predict and prevent vessel collisions based on historical movement of vessels in heavy traffic regions such as Singapore strait. We are looking for talented developers to join our development team to help us develop machine learning and agent-based simulation models to quantify vessel collision risk at Singapore strait and port. If you are data curious, excited about deriving insights from data, and motivated by solving a real-world problem, we want to hear from you.

Qualifications

A B.Sc. in a quantitative field (e.g., Computer Science, Statistics, Engineering, Science) Good coding habit in Python and able to solve problems in a fast pace Familiar with popular machine learning models Eager to learn new things and has passion in work Take responsibility, team oriented, and result oriented The ability to communicate results clearly and a focus on driving impact

More Information

Location: Kent Ridge CampusOrganization: EngineeringDepartment : Industrial Systems Engineering And ManagementEmployee Referral Eligible: NoJob requisition ID : 7334

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Research Engineer, Machine Learning job with NATIONAL UNIVERSITY OF SINGAPORE | 279415 - Times Higher Education (THE)

Since 2019, experts at the University of North Carolina at Chapel Hills University Libraries have investigated the use of machine learning to identify racist laws from North Carolinas past. Now a grant of $400,000 from The Andrew W. Mellon Foundation will allow them to extend that work to two more states. The grant will also fund research and teaching fellowships for scholars interested in using the projects outputs and techniques.

On the Books: Jim Crow and Algorithms of Resistance began with a question from a North Carolina social studies teacher: Was there a comprehensive list of all the Jim Crow laws that had ever been passed in the state?

Finding little beyond scholar and activist Pauli Murrays 1951 book States laws on race and color, a team of librarians, technologists and data experts set out to fill the gap. The group created machine-readable versions of all North Carolina statutes from 1866 to 1967. Then, with subject expertise from scholarly partners, they trained an algorithm to identify racist language in the laws.

We identified so many laws, said Amanda Henley, principal investigator for On the Books and head of digital research services at the University Libraries. There are laws that initiated segregation, which led to the creation of additional laws to maintain and administer the segregation. Many of the laws were about school segregation. Other topics included indigenous populations, taxes, health care and elections, Henley said. The model eventually uncovered nearly 2,000 North Carolina laws that could be classified as Jim Crow.

Henley said that On the Books is an example of collections as datadigitized library collections formatted specifically for computational research. In this way, they serve as rich sources of data for innovative research.

The next phase of On the Books will leverage the teams learnings through two activities:

Weve gained a tremendous amount of knowledge through this project everything from how to prepare data sets for this kind of analysis, to training computers to distinguish between Jim Crow and not Jim Crow, to creating educational modules so others can use these findings. Were eager to share what weve learned and help others build upon it, said Henley.

On the Books began in 2019 as part of the national Collections as Data: Part to Whole project, funded by The Andrew W. Mellon Foundation. Subsequent funding from the ARL Venture Fund and from the University Libraries internal IDEA Action grants allowed the work to continue. The newest grant from The Mellon Foundation will conclude at the end of 2023.

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Grant will expand University Libraries' use of machine learning to identify historically racist laws - UNC Chapell Hill

LEEDS, United Kingdom (StudyFinds.org) An artificial intelligence system is capable of spotting whether someone will have a heart attack within the next year through a routine eye scan.

A team from the University of Leeds believes this AI tool opens the door to a cheap and simple screening program for the worlds No. 1 killer. Their tests find the computer can predict patients at risk of a heart attack in the next 12 months with up to 80% accuracy. The breakthrough adds to evidence that our eyes are not just windows to the soul, but windowsto overall healthas well.

Cardiovascular diseases, including heart attacks, are the leading cause of early death worldwide and the second-largest killer in the UK. This causes chronic ill-health and misery worldwide, project supervisor Professor Alex Frangi says in auniversity release.

This technique opens-up the possibility of revolutionizing the screening of cardiac disease. Retinal scans are comparatively cheap and routinely used in many optician practices. As a result of automated screening, patients who are at high risk of becoming ill could be referred for specialist cardiac services, Frangi adds.

The system could also be used to track earlysigns of heart disease.

The retina is a small membrane at the back of the eye containing light sensitive cells. Doctors have found that changes to the tiny blood vessels canhint at vascular disease, including heart problems.

Study authors used an advanced type of AI, known as deep learning, to teach the machine to automatically read more than 5,000 eye scans. The scans come from routine eye tests during visits to opticians or eye clinics. All of the participants are part of the UK Biobank, which tracks the health of half a million adults.

Deep learning is a complex series of algorithms that enable machines to make forecasts based on patterns in data. The technique, described in the journalNature Machine Intelligence, could revolutionize heart therapy, according to the researchers.

The AI system has the potential to identify individuals attending routine eye screening who are at higher future risk of cardiovascular disease, whereby preventative treatments could be started earlier to prevent premature cardiovascular disease, says co-author Professor Chris Gale, a consultant cardiologist at Leeds Teaching Hospitals NHS Trust.

The study identified associations between pathology in the retina andchanges in the patients heart. Once the system learned each image pattern, the AI could estimate the size and pumping efficiency of the left ventricle from retinal scans alone.

This is one of the hearts four chambers. An enlarged ventricle can increase the risk of heart disease. The computer combined the estimated size of the left ventricle and its pumping efficiency with data like age and sex.

Currently, doctors determine this information using an MRI (magnetic resonance imaging) or echocardiography scans of the heart. The diagnostic tests are expensive and are often only available in a hospital. The tests can be inaccessible for many people in countries with lesser health care systems. They also increase health care costs and waiting times in wealthy nations.

The AI system is an excellent tool for unravelling the complex patterns that exist in nature, and that is what we have found the intricate pattern of changes in the retina linked to changes in the heart, adds co-author Sven Plein of the British Heart Foundation.

A recent study discovered a similar link between biologicalaging of the retina and mortality. Those with a retina older than their actual age were up to 67% more likely to die over the next decade.

South West News Service writer Mark Waghorn contributed to this report.

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AI can predict signs of a heart attack within a year from a routine eye test - KTLA Los Angeles

Work type:Full-timeLocation:Canberra, ACTCategories:Lecturer

UNSW Canberra is a campus of the University of New South Wales located at the Australian Defence Force Academy in Canberra. UNSW Canberra endeavours to offer staff a rewarding experience and offers many opportunities and attractive benefits, including:

At UNSW, we pride ourselves on being a workplace where the best people come to do their best work.

The School of Engineering and Information Technology (SEIT) offers a flexible, friendly working environment that is well-resourced and delivers research-informed education as part of its accredited, globally recognised engineering and computing degrees to its undergraduate students. The School offers programs in electrical, mechanical, aeronautical, and civil engineering as well as in aviation, information technology and cyber security to graduates and professionals who will be Australias future technology decision makers.

We are seeking a person for the role of Postdoctoral Researcher / Senior Research Fellow in the area of machine learning.

About the Role:

Role:Postdoctoral Researcher / Senior Research FellowSalary:Level B:$110,459 - $130,215 plus 17% SuperannuationTerm:Fixed-term, 12 Months, Full-time

About the Successful Applicants

To be successful in this role you will have:

In your application you should submit a 1-page document outlining how you meet the Skills and Experience outlined in the Position Description.Please clearly indicate the level you are applying for.

In order to view the Position Description please ensure that you allow pop-ups for Jobs@UNSW Portal.

The successful candidate will be required to undertake pre-employment checks prior to commencement in this role. The checks that will be undertaken are listed in the Position Description. You will not be required to provide any further documentation or information regarding the checks until directly requested by UNSW.

The position is located in Canberra, ACT. The successful candidate will be required to work from the UNSW Canberra campus.To be successful you will hold Australian Citizenship and have the ability to apply for a Baseline Security Clearance. Visa sponsorship is not available for this appointment.

For further information about UNSW Canberra, please visit our website:UNSW Canberra

Contact:Timothy Lynar, Senior Lecturer

E: t.lynar@adfa.edu.au

T: 02 51145175

Applications Close:13 February 2022 11:30PM

Find out more about working atUNSW Canberra

At UNSW Canberra, we celebrate diversity and understand the benefits that inclusion brings to the university. We aim to ensure thatour culture, policies, and processes are truly inclusive. We are committed to developing and maintaining a workplace where everyone is valued and respected for who they are and supported in achieving their professional goals. We welcome applications from Aboriginal and Torres Strait Islander people, Women at all levels, Culturally and Linguistically Diverse People, People with Disability, LGBTIQ+ People, people with family and caring responsibilities and people at all stages of their careers. We encourage everyone who meets the selection criteria and shares our commitment to inclusion to apply.

Any questions about the application process - please emailunswcanberra.recruitment@adfa.edu.au

Excerpt from:
Senior Research Associate in Machine Learning job with UNIVERSITY OF NEW SOUTH WALES | 279302 - Times Higher Education (THE)

By Margy Eckelkamp and Katie Humphreys

Machinery has amplified the workload farmers can accomplish, and technology has delivered greater efficiencies. Now, autonomy is poised to introduce new levels of productivity and fun.

Different than its technology cousins of guidance and GPS-enabled controls, autonomy relocates the operator to anywhere but the cab.

True autonomy is taking off the training wheels, says Steve Cubbage, vice president of services for Farmobile. It doesnt require human babysitting. Good autonomy is prefaced on good data and lots of it.

As machines are making decisions on the fly, companies seek to enable them to provide the quality and consistency expected by the farmer.

We could see mainstream adoption in five to 10 years. It might surprise us depending on how far we advance artificial intelligence (AI), data collection, etc., Cubbage says. Dont say it cant happen in a short time, because it can. Autosteer was a great example of quick and unexpected acceptance.

Learn more about the robots emerging on the horizon.

The NEXAT is an autonomous machine, ranging from 20' to 80', that can be used for tillage, planting, spraying and harvesting. The interchangeable implements are mounted between four electrically driven tracks.Source: NEXAT

The idea and philosophy behind the NEXAT is to enable a holistic crop production system where 95% of the cultivated area is free of soil compaction, says Lothar Fli, who works in marketing for NEXAT. This system offers the best setup for carbon farming in combination with the possibility for regenerative agriculture and optimal yield potential.

The NEXAT system carries the modules, rather than pulls them, as Fli describes, which allowed the company to develop a simpler and lighter machine that delivers 50% more power with 40% less weight. In operation, weight is transferred onto the carrier vehicle and large tracks and optimized so it becomes a self-propelled machine.

This enables the implements to be guided more accurately and with less slip, reducing fuel consumption and CO2 emissions more than 30%, he says. Because the NEXAT carries the implement, theres not an extra chassis with extra wheels. The setup creates the best precision at a high working width that reduces soil compaction on the growing areas.

In the field, the machine is driven horizontally but rotates 90 for road travel. Two independent 545-hp diesel engines supply power. The cab, which can rotate 270, is the basis for fully automated operation but enables manual guidance.

The tillage and planting modules came from Vderstad, a Swedish company. The CrossCutter disks for tillage and Tempo planter components are no different than whats found on traditional Vderstad implements.

The crop protection modules, which work like a conventional self-propelled sprayer, come from the German company Dammann. The sprayer has a 230' boom, with ground clearance up to 6.5', and a 6,340-gal. tank.

The NexCo combine harvester module achieves grain throughputs of 130 to 200 tons per hour.

A 19' long axial rotor is mounted transverse to the direction of travel and the flow of harvested material is introduced centrally into the rotor and at an angle to achieve energy efficiency. The rotor divides it into two material flows, which according to NEXAT, enables roughly twice the threshing performance of conventional machines. Two choppers provide uniform straw and chaff distribution, even with a 50' cutting width.

The grain hopper holds 1,020 bu. and can be unloaded in a minute. See the NEXAT system in action.

At the Consumer Electronics Show, John Deere introduced its full autonomy solution for tractors, which will be available to farmers later in 2022.Its tractors are outfitted with:

Farmers can control machines remotely via the JD Operations Center app on a phone, tablet or computer.

Unlike autonomous cars, tractors need to do more than just be a shuttle from point A to point B, says Deanna Kovar, product strategy at John Deere.

When tractors are going through the field, they have to follow a very precise path and do very specific jobs, she says. An autonomous 8R tractor is one giant robot. Within 1" of accuracy, it is able to perform its job without human intervention.

Artificial intelligence and machine learning are key technologies to John Deeres vision for the future, says Jahmy Hindman, John Deeres chief technology officer. In the past five years the company has acquired two Silicon Valley technology startups: Blue River Technology and Bear Flag Robotics.

This specific autonomy product has been in development for at least three years as the John Deere team collected images for its machine learning library. Users have access to live video and images via the app.

The real-time delivery of performance information is critical, John Deere highlights, to building the trust of the systems performance.

For example, Willy Pell, John Deere senior director of autonomous systems, explains even if the tractor encounters an anomaly or an undetectable object, safety measures will stop the machine.

While the initial introduction of the fully autonomous tractor showed a tillage application, Jorge Heraud, John Deere vice president of automation and autonomy, shares three other examples of how the company is bringing forward new solutions:

See the John Deere autonomous tractor launch.

New Holland has developed the first chopped material distribution system with direct measurement technology: the OptiSpread Automation System. 2D radar sensors mounted on both sides of the combine measure the speed and throw of the chopped material. If the distribution pattern no longer corresponds to the nominal distribution pattern over the entire working width, the rotational speed of the hydraulically driven feed rotors increases or decreases until the distribution pattern once again matches. The technology registers irregular chopped material distribution, even with a tailwind or headwind, and produces a distribution map.

The system received a Agritechnica silver innovation award.Source: CNH

As part of Vermeers 50th anniversary celebration in 2021, a field demonstration was held at its Pella, Iowa, headquarters to unveil their autonomous bale mover. The BaleHawk navigates through a field via onboard sensors to locate bales, pick them up and move them to a predetermined location.

With the capacity to load three bales at a time, the BaleHawk was successfully tested with bales weighing up to 1,300 lb. The empty weight of the vehicle is less than 3 tons. Vermeer sees the lightweight concept as a solution to reduce compaction.

See the Vermeer Bale Hawk in action.Source: Vermeer

In April 2021, Philipp Horsch, with German farm machinery manufacturer Horsch Machinen, tweeted about its Robo autonomous planter. He said the machine was likely to be released for sale in about two years, depending on efforts to change current regulations, which state for fully autonomous vehicle use in Germany, a person must stay within 2,000' to watch the machine.

The Horsch Robo is equipped with a Trimble navigation system and fitted with a large seed hopper. See the system in action.Source: Horsch

Katie Humphreys wears the hat of content manager for the Producer Media group. Along with writing and editing, she helps lead the content team and Test Plot efforts.

Margy Eckelkamp, The Scoop Editor and Machinery Pete director of content development, has reported on machinery and technology since 2006.

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Autonomy in Action: These Machines Bring Imagination to Life - Agweb Powered by Farm Journal