PhD Research Fellow in Integrative Machine Learning for Gene Prioritization

About the position

Position as PhD Research Fellow in Integrative Machine Learning for Gene Prioritization available at the research group of Scientific Computing and Machine Learning (SCML) at the Machine Learning Section of the Department of Informatics, the Faculty of Mathematics and Natural Sciences.

No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Starting date no later than October 1, 2024.

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.

Job description

A 3-year position with a flexible starting date, no later than October 1st, 2024, is available for a Ph.D. candidate at the intersection of machine learning and genomics in the research group of Scientific Computing and Machine Learning (SCML) at the Machine Learning Section of the Department of Informatics.

Our project explores gene prioritization, an essential aspect of systems bi-ology, where genes are meticulously detected and assessed for their potential links to diseases or phenotypes of interest. The project aims to significantly contribute to annotating overlooked genes, illuminating their functions with greater accuracy and confidence. Addressing the challenge of underperforming machine learning models in practical gene prioritization scenarios is the primary motivation behind this PhD project.

The primary focus of the PhD project is the development of integrative machine learning strategies employing multi-omics data to enhance the accuracy and efficiency of gene prioritization methods and contribute significantly to understanding the functional roles of genes in complex biological systems.

The successful candidate will be involved in conceiving, designing, developing, and implementing pipelines with supervised algorithms focused on multiomics data and guided by prior biological mechanistic knowledge through advanced ML architectures such as transformer-based models. An additional focus is placed on ensuring the explainability of ML model out-puts, fortifying transparency and interpretability. Furthermore, we aim to make methodological and theoretical contributions to the field by addressing critical challenges, including a shortage of positive samples, label uncertainty, the lack of negative labels, managing bi-ased information, and exploring appropriate evaluation metrics and strategies for gene prioritization models.

Qualification requirements

The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships 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 bioinformatics, computational biology, computer science, or a related field, with a focus on machine learning. 
• Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system
• Fluent oral and written communication skills in English

Desired qualifications:

• A strong interest in bioinformatics and a desire to utilize machine learning in computational biology
• Advanced analytical and problem-solving skills, with the capacity to analyze complex datasets and extract meaningful insights
• Strong background in machine learning
• Proficiency in programming languages such as Python and R
• Experience with data preprocessing, feature engineering, and model evaluation techniques
• Experience in at least one of the following technical fields: Linux and bash scripting, High-performance computing, Large-scale data processing, and Large-scale software development
• Proficiency in data manipulation libraries (e.g., pandas/NumPy) and machine learning frameworks (e.g., PyTorch/Tensorflow/Keras) is a plus
• Experience in omics data analysis and pathway/network analysis is highly advantageous
• Experience with explainable AI (e.g., SHAP/LIME) and data fusion is highly advantageous
• Familiarity and experience with genomics and bioinformatics are a plus but not essential

Candidates without a master’s degree have until 30 June, 2024 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 system
• The average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational system
The Master’s thesis must have the grade B or better in the Norwegian educational systemEnglish requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements

The 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 here.

Personal skills

• Excellent communication skills
• The ability to think critically and creatively to navigate challenges
• The ability to work collaboratively, cross-disciplinary aptitude
• Robust organizational and interpersonal competencies

We offer

• Salary 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 agreement 
• Vibrant international academic environment
• Career development programmes
• Oslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activities

How to apply

The application must include:

• Cover letter - statement of motivation and research interests
• CV (summarizing education, positions and academic work - scientific publications)
• Copies of the original Bachelor and Master’s degree diploma and transcripts of records
• Documentation of English proficiency if applicable
• List of publications and academic work that the applicant wishes to be considered by the evaluation committee
• Names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number)

The application with attachments must be delivered in 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 or a Scandinavian language.

Interviews with the best qualified candidates will be arranged.

Formal regulations

Please see the guidelines and regulations for appointments to Research Fellowships at the University of Oslo.

According to the Norwegian Freedom and 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.

UiO has an agreement for all employees, aiming to secure rights to research results a.o.

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 information

For further information please contact:

• Associate Professor Pooya Zakeri, phone: +47 92954475, e-mail: zakeri@ifi.uio.no

For questions regarding Jobbnorge, please contact HR Adviser Therese Ringvold, email: therese.ringvold@mn.uio.no

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