挪威Simula研究所人工智能-癌症注册系统项目博士后研究员

挪威Simula研究所人工智能-癌症注册系统项目博士后研究员

Job Description

Fornebu

Bislett

Bergen

Call for Post-Doctoral Fellow for AI-based Testing of Cancer Registry System

Fornebu

Application deadline:

30 October 2020

Project Description

The position is funded by the Research Council of Norway. The project title is: AI-Powered Testing Infrastructure for Cancer Registry System (AIT4CR). The post-doctoral fellow will be hired at Simula; however, will also spend a considerable amount of time at the Cancer Registry to work closely with the real system, data, experts, developers, and testers at the cancer registry,

The Cancer Registration Support System (CaReSS) at the Cancer Registry of Norway has been handling information on cancer since 1952. The system has gone through several upgrades and is now fully digital. All health personnel diagnosing or treating cancer patients are obliged by law to report to the Cancer Registry, and in addition, data from other registries are collected. All patient information is submitted to the system and curated by trained medical coders. They create patient histories by piecing together all this information into patients’ histories, which are timelines of the patient’s diagnostic workup, treatments, and follow-up. Hundreds of rules have been defined to validate the data. These rules are manually reviewed to ensure that patient histories are correct. New rules are constantly introduced, and existing rules are frequently revised due to new medical findings. Dependencies between rules, such as ordering and timing, also exist. The automated checking of rules is an ideal solution to improve the quality of patient history. However, not only are the rules changing over time but also the data as diagnostics and treatment are improved. This leads to the continuous evolution of the CaReSS’s key software components, to ensure that such data structures and rules are correctly specified and implemented. Thus, there is a need for a cost-effective, systematic, and automated testing layer, i.e., new testing methods implemented in a software testing tool and a test execution infrastructure—the innovation planned in this project. Such testing methods will be based on state-of-the-art artificial intelligence techniques such as deep neural networks, rule mining algorithms, and search algorithms to support cost-effective and optimized testing of continuously evolving CaReSS. Such techniques have shown promising results for testing many other types of systems, and in this project, the application of these techniques will significantly advance the current state-of-the-art of testing methods CaReSS that is commonly used in cancer registries all over the world. This innovation is expected to have a long-term impact (of several decades at least) on the quality of data that the system can provide to end-users (e.g., patients, researchers, doctors, and government officials), as the data in the Cancer Registry is used in medical research and evaluation of health services.

Candidate profile

We consider interested candidates who have a Ph.D. degree in software engineering, preferably with experience in the area of software testing and knowledge about the use of artificial intelligence techniques (e.g., reinforcement learning, search algorithm) with top grades and good publications record. The candidate will also have to demonstrate an excellent level of spoken and written English, possess good interpersonal and communication skills, and show a willingness to work as part of an international team.