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About the position

The Department of Circulation and Medical Imaging has a vacancy for a PhD candidate. This position is a fixed term, full time (100 %) position for three years starting in autumn 2022.

For a position as a PhD candidate, the goal is a completed doctoral education up to an obtained doctoral degree.

The PhD candidate will develop and validate machine learning techniques for automatic assessment of digital lung cancer tissue sections. The main goal is to achieve more patient specific management through improved prognostic differentiation in lung cancer and to provide a deeper understanding of the pathology of the disease. The basis of the project will be digitized whole slide images fromestablished, large, well-described cohorts of lung cancer patients and existing softwaredeveloped as part of the project.

The research team is cross-disciplinary and is a collaboration betweenNTNU, St. Olavs hospital, Levanger Hospital and SINTEF

The position offers challenging and varied tasks in an environment characterised by active, committed and skilled researchers with both technological and clinical expertise.

The successful candidate will be reporting to the project manager, MD and Associate Professor Hanne Sorger.

Duties of the position

Required selection criteria

The appointment is to be made in accordance with the regulations in force concerningState Employees and Civil ServantsandRegulations concerning the degrees ofPhilosophiaeDoctor (PhD)andPhilosodophiaeDoctor (PhD) in artistic researchnational guidelines for appointment as PhD, post doctor and research assistant

Preferred selection criteria

Personal characteristics

We offer

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 491 200 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 3 years.

Appointment to a PhD position requires that you are admitted to the PhD programme inMedical Technologywithin three months of employment, and that you participate in an organized PhD programme during the employment period.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

The position is subject to external funding.

It is a prerequisite you can be present at and accessible to the institution daily.

About the application

The application and supporting documentation to be used as the basis for the assessment must be in English.

Publications and other scientific work must follow the application. Please note that applications are only evaluated based on the information available on the application deadline. You should ensure that your application shows clearly how your skills and experience meet the criteria which are set out above.

The application must include the following:

If all,or parts,of your education has been taken abroad, we also ask you to attach documentation of the scope and quality of your entire education, both bachelor's and master's education, in addition to other higher education. Description of the documentation required can befoundhere. If you already have a statement fromNOKUT,pleaseattachthisas well.

Joint works will be considered. If it is difficult to identify your contribution to joint works, you must attach a brief description of your participation.

In the evaluation of which candidate is best qualified, emphasis will be placed on education,experienceand personal and interpersonalqualities.Motivation,ambitions,and potential will also countin the assessment ofthe candidates.

NTNU is committed to following evaluation criteria for research quality according toThe San Francisco Declaration on Research Assessment - DORA.

General information

Working at NTNU

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background.

The city of Trondheimis a modern European city with a rich cultural scene. Trondheim is the innovation capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world. Professional subsidized day-care for children is easily available. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life and has low crime rates and clean air quality.

As an employeeatNTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.

In accordance with The Public Information Act (Offentleglova), your name, age, position and municipality may be made public even if you have requested not to have your name entered on the list of applicants.

If you have questions about the position,please contact Associate professor at NTNU Hanne Sorger, telephone +4791816787, email hanne.sorger@ntnu.no and/or Chief Scientist at SINTEF Medical technology Thomas Lang, telephone +4790962913, e-mail thomas.lango@sintef.no

If you have any questions about the recruitment process, please contact HR consultant Julie Hoff, e-mail: julie.hoff@ntnu.no

Please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma fromChina Credentials Verification (CHSI).

If you are invited for interview you must include certified copies of transcripts and reference letters.

Application deadline: 15.02.2022

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

The Faculty of Medicine and Health Sciences (MH) is one of NTNUs largest faculties with about 1800 employees (1300 full-time equivalents).

The Facultys main activities are education and research in close integration with St Olavs Hospital. For further information, see: https://www.ntnu.edu/mh.

Deadline15th February 2022EmployerNTNU - Norwegian University of Science and TechnologyMunicipalityTrondheimScopeFulltimeDurationTemporaryPlace of serviceya, Trondheim

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PhD Candidate, Artificial Intelligence for Diagnostic Analysis of Digital Lung Cancer job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU...

Although traffic is a challenge in many cities across the globe, it is a challenge that can be managed and solved. Governments and city officials should work with the private sector to deploy intelligent solutions that not only improve the present state of roads and highways, but also prepare for the future.

Some of the solutions to traffic challenges lie in using artificial intelligence (AI), data collection and analytics, and sophisticated hardware that automates and streamlines processes to achieve high levels of accuracy, safety, and reliability.

Coupled with existing systems comprised of network cameras and monitoring solutions, the innovation and application of AI heralds a new era in traffic management.

Cities are getting bigger and smarter

Globally, our cities are growing and becoming more populous. The migration from rural to urban areas, combined with overall population growth, will see another 2.5 billion people living in cities by 2050, with 90% of this increase taking place in Africa and Asia, as per a United Nations report. This means more people travelling on more roads, and without smart interventions, this could mean more traffic.

In some areas, with this migration comes the emergence of smart cities urban areas that use ICT, AI, and data-related processes to maximise operations and investment.

In Abu Dhabi and Dubai two cities rated 28th and 29th respectively on the IMD Smart City Index 2021 traffic congestion is not considered a problem among residents. Such a result is, in part, thanks to the adoption of AI processes to deal with traffic and road management.

Managing traffic and the challenges thereof

Traffic management centres (TMCs) are the central nervous systems used for monitoring and managing traffic in an area. Imagine sprawling rooms with hundreds of surveillance cameras watching intersections, highways, and other road infrastructure for congestion, accidents, and the like to allow for quick-time responses from officials.

However, traditional TMC setups only allow for a certain level of monitoring and oversight.

Intelligent solutions, comprising both hardware and software, can fill these gaps.

Quality hardware is the first step

The strength and effectiveness of a TMC is indicated by its ability to collect data, and the application of AI requires up-to-date and robust hardware for it to achieve its full capability.

Centres use an extensive network of video management systems (VMSs) to monitor infrastructure and capture incidents in real time. This includes a range of fixed-point, panoramic, and thermal cameras hardware thats built to perform regardless of factors such as environment, object layout, and time of day.

The application of AI

To address traffic challenges, hardware is coupled with advancements in machine learning (ML), which focuses on gathering and analysing data to identify and replicate patterns in behaviour. By design, roads are ideal for this process as users conform to its rules, producing data which can be used to track deviations and create optimised models.

Deep learning (DL) takes this a step further. A sub-field of ML, DL uses raw data to automatically determine features that distinguish different sets of data from one another. The processing of data in this way does not need human intervention.

What does this look like in the context of traffic management? It automates several processes and decreases the necessity for human intervention or oversight. Data can be used to identify weak or exposed points in infrastructure or throughout the overall scope of surveillance. Roads are made safer through quicker response times from emergency services and officials.

Edge computing and edge-based analytics

Cameras capture a lot of data. A network of cameras tied to a VMS captures even more data, and with this comes the necessity of cloud or server-based data storage and processing. Edge computing offers an alternative, more effective solution to constantly transfer data from devices to the cloud.

With edge computing, captured data is analysed closer to its point of origin rather than having to go back-and-forth between servers and central data points.[1] The localisation of this process results in less dependency on other infrastructure and less time is lost due to factors such as network latency.

Urban populations may continue to grow, but traffic problems do not need to accompany this growth. Leaders can harness the power of AI combined with smart surveillance hardware as we build the cities of the future. Cities and urban areas stand to benefit from the application of AI in this manner as we head into a smart future together.

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Artificial Intelligence key to traffic solutions in cities of the future - Construction Week Online

In this paper, a comprehensive overview on current applications of AI in oncology-related areas is provided, specifically describing the AI-based devices that have already obtained the official approval to enter into clinical practice. Starting from its birth, AI demonstrated its cross-cutting importance in all scientific branches, showing an impressive growth potential for the future. As highlighted in this study, this growth has interested also oncology and related specialties.

In general, the application of the FDA-approved devices has not been conceived as a substitute of classical analysis/diagnostic workflow, but is intended as an integrative tool, to be used in selected cases, potentially representing the decisive step for improving the management of cancer patients. Currently, in this field, the branches where AI is gaining a larger impact are represented by the diagnostic areas, which count for the vast majority of the approved devices (>80%), and in particular radiology and pathology.

Cancer diagnostics classically represents the necessary point of start for designing appropriate therapeutic approaches and clinical management, and its AI-based refining represents a very important achievement. Furthermore, this indicates that future developments of AI should also consider unexplored but pivotal horizons in this landscape, including drug discovery, therapy administration and follow-up strategies. In our opinion, for determining a decisive improvement in the management of cancer patients, indeed, the growth of AI should follow comprehensive and multidisciplinary patterns. This represents one of the most important opportunities provided by AI, which will allow the correct interactions and integration of oncology-related areas on a specific patient, rendering possible the challenging purposes of personalised medicine.

The specific cancer types that now are experiencing more advantages from AI-based devices in clinical practice are first of all breast cancer, lung cancer and prostate cancer. This should be seen as the direct reflection of their higher incidence compared with other tumour types, but in the future, additional tumour types should be taken into account, including rare tumours that still suffer from the lack of standardised approaches. Since AI is based on the collection and analysis of large datasets of cases, however, the improvement in the treatment of rare neoplasms will likely represent a late achievement. Notably, if together considered, rare tumours are one of the most important category in precision oncology [11]. Thus, in our opinion, ongoing strategies of AI development cannot ignore this tumour group; although the potential benefits seem far away, it is already time to start collecting data on rare neoplasms.

One of the most promising expectancy for AI is the possibility to integrate different and composite data derived from multi-omics approaches to oncologic patients. The promising tools of AI could be the only able to manage the big amount of data from different types of analysis, including information derived from DNA and RNA sequencing. Along this line, the recent release of American College of Medical Genetics standards and guidelines for the interpretation of the sequence variants [12] has fostered a new wave of AI development, with innovative opportunities in precision oncology (https://www.businesswire.com/news/home/20190401005976/en/Fabric-Genomics-Announces-AI-based-ACMG-Classification-Solution-for-Genetic-Testing-with-Hereditary-Panels; last access 09/21/2021). In our opinion, however, the lack of ground-truth information derived from protected health- data repositories still represents a bottleneck in evaluating the accuracy of AI applications for clinical decision-making.

Overall considered, AI is providing a growing impact to all scientific branches, including oncology and its related fields, as highlighted in this study. For designing new development strategies with concrete impacts, the first steps are representing by knowing its historical background and understanding its current achievements. As here highlighted, AI is already entered into the oncologic clinical practice, but continuous and increasing efforts should be warranted to allow AI expressing its entire potential. In our opinion, the creation of multidisciplinary/integrative developmental views, the immediate comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the AI-revolution in oncology.

See more here:
Artificial intelligence in oncology: current applications and future perspectives | British Journal of Cancer - Nature.com

DUBLIN, Nov. 25, 2021 /PRNewswire/ -- The "Future Growth Potential of the Global AI Market" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence (AI) is transforming organizations, industries, and the technology landscape. The world is moving to the increased adoption of AI-powered smart applications/systems, and this trend will increase exponentially over the next few years. AI technologies are maturing, and the need to leverage their capabilities is becoming a CXO priority.

As businesses make AI part of their core strategy, the transformation of business functions, measures, and controls to ensure ethical best practices will gain importance. The implementation and the governance of ethical AI practices will become a priority and a board-level concern.

The deployment of AI solutions that are ethical (from a regulatory and a legal standpoint), transparent, and without bias will become essential. As governments and industry bodies across the world articulate AI regulations, AI companies must establish their ethical frameworks until roadmaps are clearly defined.

The operationalization of ethical AI principles is challenging for enterprises, given the large volumes of user-centric data that need to be processed, the breadth of use-cases, the regulatory variations in operating markets, and the diverse stakeholder priorities.

This also opens up opportunities for technology vendors and service providers. To effectively partner with enterprises and monetize these opportunities, ICT providers need to assess potential areas impacting AI ethics and evaluate opportunities across the people-process-technology spectrum.

Forward-thinking technology and service companies, including large ICT providers and start-ups, are working with enterprises and industry stakeholders to leverage potential opportunities. Ethical challenges will continue to be discovered and remediated to create sustained growth in potential advisory services.

As enterprises define goals, values, strategic outcomes, and key performance metrics, the time is right for technology companies to strategically partner with enterprises in the detection and the mitigation of ethical AI concerns.

Key Topics Covered:

1. Strategic Imperatives

2. Growth Environment

3. Growth Opportunity Analysis

4. Growth Opportunity Universe

For more information about this report visit https://www.researchandmarkets.com/r/l7isqw

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1904 Fax (outside U.S.): +353-1-481-1716

SOURCE Research and Markets

http://www.researchandmarkets.com

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Global AI (Artificial Intelligence) Market Report 2021: Ethical AI Practices and Advisory will be Incorporated in AI Technology Growth Strategy to...

Artificial intelligence (AI) was once the stuff of science fiction. But its becoming widespread. It is used in mobile phone technology and motor vehicles. It powers tools for agriculture and healthcare.

But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Googles Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies. For instance, in a 2018 paper Gebru and another researcher, Joy Buolamwini, had showed how facial recognition software was less accurate in identifying women and people of colour than white men. Biases in training data can have far-reaching and unintended effects.

There is already a substantial body of research about ethics in AI. This highlights the importance of principles to ensure technologies do not simply worsen biases or even introduce new social harms. As the UNESCO draft recommendation on the ethics of AI states:

We need international and national policies and regulatory frameworks to ensure that these emerging technologies benefit humanity as a whole.

In recent years, many frameworks and guidelines have been created that identify objectives and priorities for ethical AI.

This is certainly a step in the right direction. But its also critical to look beyond technical solutions when addressing issues of bias or inclusivity. Biases can enter at the level of who frames the objectives and balances the priorities.

In a recent paper, we argue that inclusivity and diversity also need to be at the level of identifying values and defining frameworks of what counts as ethical AI in the first place. This is especially pertinent when considering the growth of AI research and machine learning across the African continent.

Research and development of AI and machine learning technologies is growing in African countries. Programmes such as Data Science Africa, Data Science Nigeria, and the Deep Learning Indaba with its satellite IndabaX events, which have so far been held in 27 different African countries, illustrate the interest and human investment in the fields.

The potential of AI and related technologies to promote opportunities for growth, development and democratisation in Africa is a key driver of this research.

Yet very few African voices have so far been involved in the international ethical frameworks that aim to guide the research. This might not be a problem if the principles and values in those frameworks have universal application. But its not clear that they do.

For instance, the European AI4People framework offers a synthesis of six other ethical frameworks. It identifies respect for autonomy as one of its key principles. This principle has been criticised within the applied ethical field of bioethics. It is seen as failing to do justice to the communitarian values common across Africa. These focus less on the individual and more on community, even requiring that exceptions are made to upholding such a principle to allow for effective interventions.

Challenges like these or even acknowledgement that there could be such challenges are largely absent from the discussions and frameworks for ethical AI.

Just like training data can entrench existing inequalities and injustices, so can failing to recognise the possibility of diverse sets of values that can vary across social, cultural and political contexts.

In addition, failing to take into account social, cultural and political contexts can mean that even a seemingly perfect ethical technical solution can be ineffective or misguided once implemented.

For machine learning to be effective at making useful predictions, any learning system needs access to training data. This involves samples of the data of interest: inputs in the form of multiple features or measurements, and outputs which are the labels scientists want to predict. In most cases, both these features and labels require human knowledge of the problem. But a failure to correctly account for the local context could result in underperforming systems.

For example, mobile phone call records have been used to estimate population sizes before and after disasters. However, vulnerable populations are less likely to have access to mobile devices. So, this kind of approach could yield results that arent useful.

Similarly, computer vision technologies for identifying different kinds of structures in an area will likely underperform where different construction materials are used. In both of these cases, as we and other colleagues discuss in another recent paper, not accounting for regional differences may have profound effects on anything from the delivery of disaster aid, to the performance of autonomous systems.

AI technologies must not simply worsen or incorporate the problematic aspects of current human societies.

Being sensitive to and inclusive of different contexts is vital for designing effective technical solutions. It is equally important not to assume that values are universal. Those developing AI need to start including people of different backgrounds: not just in the technical aspects of designing data sets and the like but also in defining the values that can be called upon to frame and set objectives and priorities.

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Defining what's ethical in artificial intelligence needs input from Africans - The Conversation CA