BeskrivelseJob descriptionPosition as PhD Research Fellow in Software Engineering available at the department of Informatics.No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Starting date is as soon as possible.The fellowship period is three (3) years. A fourth year may be considered with a workload of 25 % that may consist of teaching, supervision duties, and/or research assistance. This is dependent upon the qualification of the applicant and the current needs of the department.Knowledge development in a changing world - Science and technology towards 2030Faculty of Mathematics and Natural SciencesMore about the positionShort version:Project title: Improving Software Quality with Advanced Machine LearningThe goal of the Ph.D. project is to leverage the power of cutting-edge Machine Learning methods, including Large Language Models (LLMs) to enhance software development practices. The results will provide real-time, actionable insights to help the teams make their software development sustainable by prioritizing quality and managing technical debt. Background: Software is becoming more complex every day, and the need to deliver faster (in a matter of hours if not minutes) is pushing software organizations and teams to their limit. Sub-optimal decisions in prioritizing solid software are sometimes taken, creating technical debt, which increases the risk of a huge impact in the medium-long run. Recognizing in real time the accumulation and impact of the most dangerous issues is becoming vital. Unfortunately, current available approaches are incomplete and do not provide valuable insights [1].  The advent of powerful Machine Learning approaches can enhance software practices by giving useful insights based on knowledge mined from public and commercial archives, as shown in our paper [2]. Examples of powerful and promising technologies are Large Language Models (LLMs). Especially their fine-tuning with additional knowledge, or the creation of embeddings for domain-specific use cases to increase reliability and accuracy can greatly improve software development. [1] P. C. Avgeriou et al., "An Overview and Comparison of Technical Debt Measurement Tools," in IEEE Software, 2021[2] D. Skryseth, K. Shivashankar, I. Pilán and A. Martini, "Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers" 2023 ACM/IEEE International Conference on Technical Debt (TechDebt), Melbourne, Australia, 2023Main task:The position will assure the candidate a concrete goal to pursue but also a high degree of flexibility when choosing technologies and approaches to reach such goal. A typical plan for the candidate would be to:1)    collect existing data from several data sources available publicly or provided by large software organizations (codebase, project management, etc.)2)    experiment and compare state-of-the-art Machine Learning methods to analyze and aggregate data from different sources following a thorough scientific approach3)    create useful insights (measures or visualizations) for the software development teams, architects, and managers to take decisions on how to prioritize security and technical debt 4)    when possible, provide novel empirical and theoretical insights in how technical debt is managed5)    evaluate insights in practice with the practitioners using a combination of qualitative and quantitative data collection6)    the previous steps will be continuously iterated, using the Design Science Research methodology Funding:The position is mostly funded by the department of informatics of UiO. A small part of it might be covered by the grant awarded by the Research Council of Norway (under the Innovation Project for the Industrial Sector 2022 program) supporting the following project: Data-driven continuous management of technical debts for sustainable software development – TechDebtOps. The recipient of such grant is a consortium including Visma (project owner), UiO, Sintef, Akva group, and Knowit. There is a possibility to be engaged and participate in such project.Enrollment:The PhD candidate will be hired at UiO, Software Engineering group, at the department of Informatics. There is a possibility to work closely with our industrial partners in the above-mentioned project. Supervision and collaboration:The main supervisor will be professor Antonio Martini, who has more than 10 years of experience and more than 50 publications on the technical debt topic (see papers here, citations here and linkedin profile here). He is also supervising another PhD student enrolled in the abovementioned project as well as a number of master students working on applying Machine Learning for data-driven management of technical debt. This position will give the opportunity to collaborate with a thriving environment contributing to a related (yet not identical) topic, which can provide ideas and technical support for the candidate, as well as an engaging group for social interactions.Prof. Martini has several ongoing collaborations with companies in the Nordics and in Europe and collaborates with several international Universities. There will be opportunities to collaborate with researchers all over the world.Prof. Martini is very active in the research community, with several roles as Program and General Chair for relevant conferences related to the topic of the position. He will assure solid supervision on software engineering techniques and will provide the candidate with access to a broad academic and industrial network. This will assure that the PhD student will have great opportunities to publish papers in top conferences and journals. A co-supervisor will be appointed when the candidate is hired. Besides supervision and collaboration with the partners in the project, the PhD candidate will work in the context of the Software Engineering group at UiO. In particular, the PhD candidate can count on access to knowledge and expertise related to Machine Learning, Agile Software Development, DevOps, Team Collaboration, quantitative data analysis, qualitative data analysis, and case-study research.Further opportunities and future employability of the candidate:This research is critical to improving the ways of working of large software organizations providing everyday critical products and services for several users around the world. The candidate will acquire specialized knowledge that is (and will be) critical for many years in the future, relevant both for industrial and academic careers. The contact with the industry during the project will also give possibilities for future collaborations. Finally, the results are intended to be commercialized after the end of the project (for example, a candidate is the ACDtek startup funded by prof. Martini), which could create additional opportunities.Qualification requirementsThe Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for this fellowship will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.Required qualifications:Master’s degree or equivalent in Software Engineering or Data ScienceForeign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational systemDesired qualifications: If the applicant has a degree in Software Engineering, documented knowledge or experience in data analysis (even better the use of Machine Learning) it will be considered an advantageIf the applicant has a degree in Data Science, documented knowledge or experience in software development will be considered an advantageKnowledge of the Norwegian language is not necessary but can be considered an advantageCandidates without a Master’s degree have until December 2023 to complete the final exam.Grade requirements:The norm is as follows:the average grade point for courses included in the Bachelor’s degree must be C or better in the Norwegian educational systemthe average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational systemthe Master’s thesis must have the grade B or better in the Norwegian educational systemFluent oral and written communication skills in EnglishEnglish requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements:https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8The purpose of the fellowship is research training leading to the successful completion of a PhD degree.The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be submitted to the department no later than two months after taking up the position. For more information see:http://www.uio.no/english/research/phd/http://www.mn.uio.no/english/research/phd/Personal skillsAbility to receive and provide constructive and critical feedbackGood attitude towards conducting some technical and practical implementationGood writing skills are appreciatedWe offerSalary NOK 532 200– 575 400 per year depending on qualifications and seniority as PhD Research Fellow (position code 1017)Attractive welfare benefits and a generous pension agreementVibrant international academic environmentCareer development programmesOslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activitiesHow to applyThe application must include:Cover letter - statement of motivation and research interestsCV (summarizing education, positions and academic work - scientific publications)Copies of the original Bachelor and Master’s degree diploma, transcripts of records andLetters of recommendationDocumentation of English proficiencyList of publications and academic work that the applicant wishes to be considered by the evaluation committeeNames and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number)The application with attachments must be submitted to our electronic recruiting system (please follow the link “Apply for this job”). Foreign applicants are advised to attach an explanation of their University's grading system. Please note that all documents should be in English.Applicants will be called in for an interview.Formal regulationsPlease see the guidelines and regulations for appointments to Research Fellowships at the University of Oslo.No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.According to the Norwegian Freedom of Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure. The University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and perspectives.If there are qualified applicants with disabilities, employment gaps or immigrant background, we will invite at least one applicant from each of these categories to an interview.Contact informationFor further information please contact: Professor Antonio Martini, e-mail: antonima@ifi.uio.noFor questions regarding Jobbnorge, please contact HR Adviser Therese Ringvold,  e-mail: therese.ringvold@mn.uio.no

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