PhD student in Computing Science: Umeå University 1447 views

PhD student in Computing Science with focus on responsible machine learning

Umeå University, Faculty of Science and Technology

Umeå University is one of Sweden’s largest institutions of higher education with over 35,000 students and 4,200 faculty and staff. We are characterised by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison. Umeå University is also the site of the pioneering discovery of the CRISPR-Cas9 genetic scissors – a revolution in genetic engineering that has been awarded the Nobel Prize in Chemistry.

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The Department of Computer Science, characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison, is looking for a Doctoral student in computing science with a focus on responsible AI with learning from multiple representations.

The Department of Computing science has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. The 60 Doctoral students within the department consists of a diverse group from different nationalities, background and fields. If you work as a Doctoral student with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions.

Is this interesting for you? Welcome with your application before October 17, 2022.

Project description

Learning with multiple representation (LMR) refers to machine learning methods that leverage several representations of the data and models or several formalizations of the learning task simultaneously, orchestrate them in a coordinated fashion, and use them in a synergistic way to solve a single problem. Multiple representations can interact in different ways and exchange information at various levels (e.g., data, model, prediction) and stages of the learning process. For example, an LMR system may have a parallel architecture, meaning that learning is essentially carried out in parallel (independently of each other), and predictions are only fused in the very end. However, these methods lack integration of responsible AI in terms of traceability of socio-ethical requirements, constraints, bias, fairness, transparency, and interoperability in designing and deploying frameworks in real-time AI systems.

This project aims to investigate, develop and integrate responsible AI framework with learning from multiple representations by taking advantage of the complementarity, the redundancy, and specific characteristics of different representations. LMR seeks to improve performance as well as ensure responsible learning from multiple representations in comparison to methods operating on a single representation only. Typical examples of LMR include the combination of numerical and symbolic formalisms, the representation of data and models on different levels of abstraction, and the combination of different types of supervision of the learner. Hence, this project plans to design, develop and evaluate novel methods and metrics that evaluate the trade-offs between computational and ethical requirements, and further analyses the bias, inclusion, transparency, and interpretability of LMR algorithms when deploying in real-time AI systems.

The successful candidate will contribute to the Social and Ethical Artificial Intelligence group and Autonomous Distributed Systems group at the Department of Computing Science within, collaborating with researchers in, e.g., machine learning, mathematical statistics, optimization, responsible AI or social AI.

This position is a part the European project LEMUR (Learning with Multiple Representations) funded by Horizon-MSCA-DN (MARIE Skłodowska-CURIE ACTIONS). The objective of MSCA Doctoral Networks is to implement doctoral programmes by partnerships of organisations from different sectors across Europe and beyond to train highly skilled doctoral candidates, stimulate their creativity, enhance their innovation capacities and boost their employability in the long-term.

Admission requirements 

The general admission requirements for doctoral studies are a second-cycle level degree or completed course requirements of at least 240 ECTS credits, of which at least 60 ECTS credits are at second-cycle level, or have an equivalent education from abroad or equivalent qualifications. To fulfil the specific entry requirements for doctoral studies in computing science, the applicant is required to have completed at least 90 ECTS credits in computing science. Applicants who otherwise have acquired skills that are deemed equivalent are also eligible.

Documented knowledge and a solid background in machine learning and responsible AI or machine learning and ethical AI is a requirement. It is also a requirement is English proficiency, at least European C1 level or equivalent level. The research is to a large extent interdisciplinary, and a broad competence profile and experience from other relevant areas (such as machine learning, distributed learning, responsible AI, deep learning, discrete optimization, and formal methods) is considered a merit.

Important personal qualities are, besides creativity and a curious mind, the ability to work both independently and in a group and experience in scientific interaction with researchers from other disciplines and in other countries.

Eligibility for the MSCA programme:

•        The candidate cannot hold a PhD degree obtained prior to the time of recruitment.
•        The candidate must not have resided or carried out his/her main activity (work, studies, etc.) in the country of the host institute for more than 12 months in the 3 years immediately prior to the recruitment. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.

For more information on the MCSA doctoral networks, see at: https://marie-sklodowska-curie-actions.ec.europa.eu/actions/doctoral-networks

The employment will start as soon as possible or as otherwise agreed.

About the position

The position provides you with the opportunity to pursue PhD studies in Computing Science for four years, with the goal of achieving the degree of Doctor in Computing Science. The position is mainly devoted to PhD studies.

The procedure for recruitment for the position is in accordance with the Higher Education Ordinance (chapter 12, 2§) and the decision regarding the position cannot be appealed.

The expected starting date is January 15, 2023 or as otherwise agreed.

Application

Applications must be submitted electronically using the e-recruitment system of Umeå University.

A complete application should contain the following documents:

•      A cover letter including a description of your research interests, your reasons to apply for the position, and your contact information. Generic cover letters, or applications without cover letter will not be considered.
•       A curriculum vitae
•       Reprints / copies of completed BSc and/or MSc theses and other relevant publications, if any
•       Authenticated copies of degree certificates, including documentation of completed academic courses and obtained grades
•       Documentation and description of other relevant experiences or competences.

The application must be written in English or Swedish. Attached documents must be in pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than 2022-10-17.

Selected applicants will be invited for an interview round, including a computing and programming assignment.

Umeå University wants to offer an equal environment where open dialogue between people with different backgrounds and perspectives lay the foundation for learning, creativity and development. We welcome people with different gender, backgrounds and experiences to apply for the current employment.

For additional information, please contact Assist. Prof. Monowar Bhuyan (monowar@cs.umu.se) or Prof. Virginia Dignum (virginia@cs.umu.se)

We look forward to receiving your application!

Umeå University strives to offer an equal environment where open dialogue between people with different backgrounds and perspectives lay the foundation for learning, creativity and development. We welcome people with different backgrounds and experiences to apply for the current employment.

We kindly decline offers of recruitment and advertising help.

Contact details:

Further information can be obtained from Assistant Professor Monowar Bhuyan, (email: monowar@cs.umu.se) and Professor Virginia Dignum (email: virginia@cs.umu.se).

Other Important Information: