Norway Job Openings
UiT Norges Arktiske Universitet
PhD Fellow in knowledge-driven machine learning
Tromsø
FULL TIME
October 15, 2024
The position
Join Integreat, Norwegian centre of excellence with a community of ambitious researchers from the fields of machine learning, statistics, logic, language technology, and ethics. Integreat is the Norwegian Centre for Knowledge-Driven Machine Learning and is seeking to recruit a fulltime Ph D student at Ui T The Arctic University of Norway for across-disciplinary project across machine learning, statistics and logic, which is ambitious, timely, and contributing to a new foundation of machine learning.
About the position:
The position is for a period of four years. The nominal length of the Ph D programme is three years. The fourth year is distributed as 25 % each year and will consist of teaching and other duties. The objective of the position is to complete research training to the level of a doctoral degree.
Admission to a Ph D program is a prerequisite for employment. The workplace is at the Department of Physics and Technology at Ui T in Tromsø. You must be able to start in the position in Tromsø within a reasonable time after receiving the offer.
About the position:
The position is for a period of four years. The nominal length of the Ph D programme is three years. The fourth year is distributed as 25 % each year and will consist of teaching and other duties. The objective of the position is to complete research training to the level of a doctoral degree.
Admission to a Ph D program is a prerequisite for employment. The workplace is at the Department of Physics and Technology at Ui T in Tromsø. You must be able to start in the position in Tromsø within a reasonable time after receiving the offer.
The studentship affiliation
Integreat is a Centre of Excellence, funded by the Research Council of Norway. Integreat has two branches, one in Tromsø (Ui T The Arctic University of Norway) and one in Oslo (University of Oslo, Ui O). Machine learning is the mathematical and computational engine of Artificial Intelligence, and therefore a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world.
Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. This will be done by combining the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technologies, ethics and machine learning, in new and unique ways.
Focus of Integreat is to develop ground-breaking methods and theories, and by this solve fundamental problems in science, technology, health and society. Integreat draws on the research strengths of researchers and students from the Machine Learning Group at Ui T, with members from the Departments of Physics and Technology, Mathematics and Statistics, and Computer Science, and from researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at Ui O, and the Norwegian Computing Centre (NR).
Integreat conducts research within fundamental machine learning. The research objectives of Integreat are:
Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. This will be done by combining the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technologies, ethics and machine learning, in new and unique ways.
Focus of Integreat is to develop ground-breaking methods and theories, and by this solve fundamental problems in science, technology, health and society. Integreat draws on the research strengths of researchers and students from the Machine Learning Group at Ui T, with members from the Departments of Physics and Technology, Mathematics and Statistics, and Computer Science, and from researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at Ui O, and the Norwegian Computing Centre (NR).
Integreat conducts research within fundamental machine learning. The research objectives of Integreat are:
- Accurate Knowledge-driven Machine Learning
- Sustainable and Green Machine Learning
- Fair, Explainable and Trustworthy Machine Learning
- Machine Learning with Quantified Uncertainty
Field of research and the role of the Ph D Fellow
The focus of this Ph D fellowship lies on method development in representation learning for graphs, similarity measures and clustering methods for graphs. Relationship graphs extracted from data have the potential to describe correlations and dependencies among objects in a dataset beyond pairwise interactions, and are crucial to quantify complex relationships in e.g., spatial omics. Generally, these graphs may vary in number of nodes and edges, and additionally carry labels on the nodes, which makes comparison of such graphs very challenging. In this project we want to find novel ways how to incorporate knowledge of underlying semantic relationships between these labels by means of representation learning, knowledge graph embeddings and exploiting non-Euclidean geometries to learn graph similarity measures.
We expect that you will engage in collaborative research with members of the machine learning group at Ui T and the members of the Integreat community from both Ui T, the University of Oslo, and the Norwegian Computing Centre. You will contribute to the centre’s seminars and be part of a network of young researchers in fundamental machine learning.
We expect that you will engage in collaborative research with members of the machine learning group at Ui T and the members of the Integreat community from both Ui T, the University of Oslo, and the Norwegian Computing Centre. You will contribute to the centre’s seminars and be part of a network of young researchers in fundamental machine learning.
Qualifications
We are looking for a motivated candidate who is independent thinking and enjoys working in a team. The suitable candidate should have expertise in deep learning and a strong documented background in mathematics is needed. Special emphasis will be given to candidates with prior experience in the above-mentioned topics.
This position requires a master's degree in physics, mathematics/statistics, computer science, or similar, or a corresponding foreign master's degree recognized as equivalent to a Norwegian master's degree. If you are near completion of your master’s degree, you may still apply.
You must document significant coursework in machine learning, pattern recognition, statistics, deep learning, and programming skills. Coursework in signal processing and physics will be considered a plus.
The suitable candidate must have:
We will also emphasize motivation and personal suitability for the position.
Other required qualification skills include:
As many people as possible should have the opportunity to undertake organized research training. If you already hold a Ph D or have equivalent competence, we will not appoint you to this position.
This position requires a master's degree in physics, mathematics/statistics, computer science, or similar, or a corresponding foreign master's degree recognized as equivalent to a Norwegian master's degree. If you are near completion of your master’s degree, you may still apply.
You must document significant coursework in machine learning, pattern recognition, statistics, deep learning, and programming skills. Coursework in signal processing and physics will be considered a plus.
The suitable candidate must have:
- Master's degree (Norwegian one or equivalent) or currently working on finalizing their master thesis (at least 30 ECTS) in machine learning, computer science, statistics, mathematics or related fields with a strong background in deep learning, pattern recognition, and graph theory.
- For this Ph D position, the Master’s thesis must have the grade C or better in the Norwegian educational system, when the degree is obtained.
- Strong programming experience and experience with training deep neural networks (e.g. Py Torch, Tensorflow).
- Candidates without a master’s degree must complete the final exam of their master studies by early 2025
- Applicants must document fluency of in English and be able to work in an international environment. Nordic applicants can document their English capabilities by attaching their high school diploma. Working knowledge of Norwegian or a Scandinavian language is also beneficial.
We will also emphasize motivation and personal suitability for the position.
Other required qualification skills include:
- Independence and self-motivation
- Creativity and ability to think outside the box
- Excellent work ethics and commitment to the job
As many people as possible should have the opportunity to undertake organized research training. If you already hold a Ph D or have equivalent competence, we will not appoint you to this position.
Admission to the Ph D programme
For employment in the Ph D position, you must be qualified for admission to the Ph D programme at the Faculty of Science and Technology and participate in organized doctoral studies within the employment period.
Admission normally requires:
Admission normally requires:
- A bachelor's degree of 180 ECTS and a master's degree of 120 ECTS, or an integrated master's degree .
In order to gain admission to the programme, the applicant must have a grade point average of C or better for the master’s degree and for relevant subjects of the bachelor’s degree. A more detailed description of admission requirements can be found here.
Applicants with a foreign education will be subjected to an evaluation of whether the educational background is equal to Norwegian higher education, following national guidelines from NOKUT. Depending on which country the education is from, one or two additional years of university education may be required to fulfil admission requirements, e.g. a 4-year bachelor's degree and a 2-year master's degree.
If you are employed in the position, you will be provisionally admitted to the Ph D programme. Application for final admission must be submitted no later than two months after taking up the position.
Applicants with a foreign education will be subjected to an evaluation of whether the educational background is equal to Norwegian higher education, following national guidelines from NOKUT. Depending on which country the education is from, one or two additional years of university education may be required to fulfil admission requirements, e.g. a 4-year bachelor's degree and a 2-year master's degree.
If you are employed in the position, you will be provisionally admitted to the Ph D programme. Application for final admission must be submitted no later than two months after taking up the position.
Inclusion and diversity
Ui T The Arctic University of Norway is working actively to promote equality, gender balance and diversity among employees and students, and to create an inclusive and safe working environment. We believe that inclusion and diversity are a strength, and we want employees with different competencies, professional experience, life experience and perspectives.
If you have a disability, a gap in your CV or immigrant background, we encourage you to tick the box for this in your application. If there are qualified applicants, we invite at least one in each group for an interview. If you get the job, we will adapt the working conditions if you need it. Apart from selecting the right candidates, we will only use the information for anonymous statistics.
If you have a disability, a gap in your CV or immigrant background, we encourage you to tick the box for this in your application. If there are qualified applicants, we invite at least one in each group for an interview. If you get the job, we will adapt the working conditions if you need it. Apart from selecting the right candidates, we will only use the information for anonymous statistics.
Ui T
We offer
Norwegian health policy aims to ensure that everyone, irrespective of their personal finances and where they live, has access to good health and care services of equal standard. As an employee you will become member of the National Insurance Scheme which also include health care services.
- Involvement in an interesting research project within a highly innovative centre environment
- Opportunities to travel and meet other leading scientists within the field
- Good career opportunities
- A good academic environment with dedicated colleagues
- Flexible working hours and a state collective pay agreement
- Pension scheme through the state pension fund
- Ph D Fellows are normally given a salary of 532 200 NOK/year with a 3% yearly increase
- If you have to relocate to Tromsø then the Faculty of Science and Technology may reimburse your moving costs. Further details regarding this matter will be made available if you receive an offer from us.
Norwegian health policy aims to ensure that everyone, irrespective of their personal finances and where they live, has access to good health and care services of equal standard. As an employee you will become member of the National Insurance Scheme which also include health care services.
Application
Your application must include:
All documentation to be considered must be in a Scandinavian language or English. Diplomas and transcripts must also be submitted in the original language, if not in English or Scandinavian. If English proficiency is not documented in the application, it must be documented before starting in the position. We only accept applications and documentation sent via Jobbnorge within the application deadline.
- Cover letter explaining your motivation and highlighting your background and its relevance to the announced position
- CV (containing a complete overview of education, supervised professional training and professional work).
- Diploma for bachelor's and master's degree
- Transcript of grades/academic record for bachelor's and master's degree
- Explanation of the grading system for foreign education (Diploma Supplement if available)
- Documentation of English proficiency
- 3 references with contact information, preferably including your master thesis supervisor
- Master’s thesis (or draft of master thesis if it is not completed), and any other academic works if you have any.
All documentation to be considered must be in a Scandinavian language or English. Diplomas and transcripts must also be submitted in the original language, if not in English or Scandinavian. If English proficiency is not documented in the application, it must be documented before starting in the position. We only accept applications and documentation sent via Jobbnorge within the application deadline.
General information
The appointment is made in accordance with State regulations and guidelines at Ui T. At our website, you will find more information for applicants.
Remuneration for the position of Ph D Fellow is in accordance with the State salary scale code 1017. A compulsory contribution of 2 % to the Norwegian Public Service Pension Fund will be deducted. You will become a member of the Norwegian Public Service Pension Fund, which gives you many benefits in addition to a lifelong pension: You may be entitled to financial support if you become ill or disabled, your family may be entitled to financial support when you die, you become insured against occupational injury or occupational disease, and you can get good terms on a mortgage. Read more about your employee benefits at: spk.no.
Remuneration for the position of Ph D Fellow is in accordance with the State salary scale code 1017. A compulsory contribution of 2 % to the Norwegian Public Service Pension Fund will be deducted. You will become a member of the Norwegian Public Service Pension Fund, which gives you many benefits in addition to a lifelong pension: You may be entitled to financial support if you become ill or disabled, your family may be entitled to financial support when you die, you become insured against occupational injury or occupational disease, and you can get good terms on a mortgage. Read more about your employee benefits at: spk.no.
A shorter period of appointment may be decided when the Ph D Fellow has already completed parts of their research training programme or when the appointment is based on a previous qualifying position Ph D Fellow, research assistant, or the like in such a way that the total time used for research training amounts to three years.
We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to the Personal Data Act information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure.
We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to the Personal Data Act information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure.
Eallju - Developing the High North
Ui T The Arctic University of Norway is a multi-campus comprehensive university at the international forefront. Our vision is to be a driving force for developing the High North. The Northern Sami notion eallju, which means eagerness to work, sets the tone for this motive power at Ui T. Along with students, staff and the wider community, we aim to utilise our location in Northern Norway and Sápmi, our broad and diverse research and study portfolio and interdisciplinary advantage to shape the future.
Our social mission is to provide research-based education of high quality, perform artistic development and carry out research of the highest international quality standards in the entire range from basic to applied. We will convey knowledge about disciplines and contribute to innovation. Our social mission unites Ui T across various studies, research fields and large geographical distances. This demands good cooperation with trade and industry and civil society as well as with international partners. We will strengthen knowledge-based and sustainable development at a regional, national and international level.
Our social mission is to provide research-based education of high quality, perform artistic development and carry out research of the highest international quality standards in the entire range from basic to applied. We will convey knowledge about disciplines and contribute to innovation. Our social mission unites Ui T across various studies, research fields and large geographical distances. This demands good cooperation with trade and industry and civil society as well as with international partners. We will strengthen knowledge-based and sustainable development at a regional, national and international level.
Academic freedom and scientific and ethical principles form the basis for all Ui T’s activities. Participation, co-determination, transparency and good processes will provide the decision-making basis we need to make wise and far-sighted priorities. Our students and staff will have the opportunity to develop their abilities and potential. Founded on academic integrity, we will be courageous, committed and generous in close contact with disciplines, people and contemporary developments.
We will demonstrate adaptability and seek good and purposeful utilisation of resources, so we are ready to meet the expectations and opportunities of the future. We will strengthen the quality and impact of our disciplines and core tasks through the following three strategic priority areas.
We will demonstrate adaptability and seek good and purposeful utilisation of resources, so we are ready to meet the expectations and opportunities of the future. We will strengthen the quality and impact of our disciplines and core tasks through the following three strategic priority areas.
Questions about the position
Elisabeth Wetzer
Associate Professor
+47 77 64 48 74
elisabeth.wetzer@uit.no
Associate Professor
+47 77 64 48 74
elisabeth.wetzer@uit.no
Deadline
16th October 2024
16th October 2024
Employer
Ui T The Arctic University of Norway
Ui T The Arctic University of Norway
Municipality
Tromsø - Romsa
Scope
Fulltime
Fulltime
Duration
Fixed Term
Fixed Term
Place of service
Hansine Hansens veg18, 9019 Tromsø
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