LEDIG STILLING VED HØGSKOLEN I INNLANDET

PhD position in machine learning solutions for rapid diagnostics of infection and antimicrobial resistance

Deadline: 04.12.2022

Inland Norway University of Applied Sciences

Inland Norway University of Applied Sciences (INN University) is home to over 14,000 students and 1,000 employees, and has campuses in Lillehammer, Hamar, Elverum, Rena, Evenstad and Blæstad.


INN University aspires to build strong and enduring academic and research environments that will spearhead regionally, nationally and internationally. We are developing a new and better institution with high academic and pedagogical quality, aiming at achieving university accreditation by 2020.


The campus is located in the city of Hamar, an urban region with 80,000 inhabitants. Hamar is a little over 1 hour from Oslo and less than 1 hour from Norway's main airport. The campus has about 3,000 students and 300 employees. With its stable climate, varied nature, and rich cultural heritage, the region can offer experiences all year round. The location between Lake Mjøsa and the mountain ranges means everything is suitable for hiking, cycling, golf, fishing, or skiing - cross-country skiing or alpine skiing.


Our vision is "Stronger Together".

PhD position in machine learning solutions for rapid diagnostics of infection and antimicrobial resistance

About the position

Department of Biotechnology, Faculty of Applied Ecology, Agriculture Sciences and Biotechnology (ALB), at Inland Norway University of Applied Sciences (INN), invites applications from outstanding candidates for a Ph.D. fellowship. The position will run for 3-years, starting in early 2023. The candidate will be enrolled in INN’s Ph.D. program in applied ecology and biotechnology, with a workplace at Hamar.

The appointed candidate will work in a proliferating scientific environment consisting of academic staff members, Ph.D./Postdoctoral fellows, and students with scientific expertise ranging from theoretical methods (machine learning, biostatistics, bioinformatics, and business analytics) to experimental methods (genomics, photonics, spectroscopy, and clinical microbiology).

The candidate’s main supervisor will be Professor Rafi Ahmad, and the supervisory group will include Dr. Manfred Grabherr (INN and Uppsala University, Sweden), Professor Sumeet Mahajan (INN and University of Southampton, UK), and Professor Gudbrand Lien (INN). The project involves national and international research collaborations, including The Arctic University of Norway (UiT), Harvard University (USA), University of Southampton (UK), Indian Institute of Technology (Delhi, India), and further research collaboration with clinicians at Inland Hospital Trust (Norway) and All India Institute of Medical Sciences (Delhi, India).

As part of the project, the Ph.D. candidate will potentially be on short terms visits (up to 3 months) at our collaboration partners' labs at Harvard University and the Indian Institute of Technology – Delhi.

The Ahmad group (https://www.inn.no/english/find-an-employee/rafi-ahmad.html) is also part of the Norwegian Network for One Health Resistome Surveillance (NORSE) – A national network of 13 Norwegian institutions, and the National Centre of Expertise – Heidner Biocluster.

About the project

The WHO considers bacterial infection due to antimicrobial-resistant (AMR) to be a critical global threat to modern healthcare and societal well-being. It’s been 34+ years since a new class of antibiotics was introduced to the market. This, together with the increased and inappropriate use of existing drugs, means that we are heading towards a world where antibiotics are no longer effective. Remarkably, most prescribing, supply, and administration of antibiotics are still carried out in the absence of any information about the nature of the infection.

The long-term objective of AMR-Diag is to develop a decision-making diagnostic system for rapid, accurate, sensitive, and PoC detection of bacterial infection, pathogen ID, characterization of its resistance profile, and prediction of antibiotic susceptibility.

The candidate will also work closely with the project group of the recently funded Norwegian Research Council project, the OH-AMR-Diag. Please find the details here.

The main tasks of this Ph.D. project are to (a) Demonstrate Machine learning (ML)/Artificial Intelligence (AI)-based methods for the identification of bacterial strain-level ID and AMR variant; Prediction of antibiotic susceptibility and minimum inhibitory concentration (MIC) for different classes of antibiotics. (b) Development of new ML/AI algorithms that combine data from different measurement techniques (photonics and genomics) for early characterization of infection, prediction of disease progression, and resistance. (c) Use ML/AI to predict the impact of rapid diagnostics tools to evaluate the burden on the healthcare systems.

The Ph.D. candidates' competence development and implementation ability are ensured by the research groups' interdisciplinary and complementary experience.

Please refer to our latest publications, which highlight our work.

https://doi.org/10.1016/j.csbj.2021.03.027

https://doi.org/10.3389/fmicb.2022.822402

https://doi.org/10.1101/2022.02.08.479540

https://doi.org/10.1101/2022.07.07.499154

https://doi.org/10.1038/s41598-020-64616-x

Qualifications

It is a requirement that the PhD research fellow qualifies for admission to the University's PhD programme in Biotechnology. Applicants who already hold a PhD will not be considered.

To be admitted to the doctoral program, the applicant must normally have a minimum master's degree or master's level (120 credits, §3 master's in the Norwegian system) or equivalent education, within computer science, bioinformatics, biostatistics, mathematics, or similar subjects. Also, applicants with a Master's degree in a relevant life science discipline and who have demonstrated the ability to learn machine learning methods and programming are encouraged to apply.

Furthermore, you must have a strong academic background from your previous studies, ie. the average grade should normally be B or better from the master's program (120 credits) or equivalent education. Calculation of the average grade is based on the credits for each course and for the master's thesis. Applicants with weaker grades than what is normally required for admission must document that they will be able to complete a doctoral degree. In cases where the education has been approved with the use of the grades passed / failed, the applicant is admitted after an individual assessment. If you have education from abroad, you can contact NOKUT for approval of your education, alternatively a similar assessment will be made in connection with the application process.

Required Qualifications

  • Experience with mathematical or statistical modeling of experimental biological data from life science and/or physical research.
  • Hands-on experience with algorithm development and knowledge of machine learning algorithms is required.
  • Experience with scripting in Python.
  • Hands-on experience with shell scripting and Linux system.

Desired Qualifications

  • Fluency in scripting in R and/or Julia.
  • Basic understanding of biological science principles, preferably antimicrobial resistance.
  • Experience with analyzing large-scale next-generation sequence data, especially from whole-genome sequencing and metagenomics.
  • Additionally, or alternatively, experience with handling large-scale spectroscopy would be helpful.
  • Hands-on experience with the use of Matlab.

Language proficiency:

  • Applicants must be proficient in both written and oral English Applicants from non-English-speaking countries must document English competence through an approved test (TOEFL, IELTS, Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE).

Evaluation of candidates for the position will be based on a total assessment of educational background, experience and personal suitability, as well as motivation and other eligibility requirements, as defined in the advertisement. In addition, the following will be emphasized: quality of the project description, documented independent research and development work or experience relevant to the project.

How to apply

Your application must include:

  • Application letter including a brief account of the applicant's research interests and motivation for applying for the position
  • Proposed project description of the study (max 8 pages, including references), outlining its academic relevance, methods, challenges and possibilities, and containing a tentative progression plan. The project description must be written in English.
  • CV detailing your relevant educational background and experience (registered in Jobbnorge's form).
  • Copies of academic diplomas and transcripts (A certified English translation of all educational documents is required unless the documents are in Norwegian.)
  • A list of publications.
  • The names and contact information for two referees.
  • Any other documentation you would like us to consider.

Attachments must be uploaded as separate files. If the attachments exceed 30 MB, they must be compressed prior to upload. It is the applicant's responsibility to ensure that all attachments are uploaded. Documents submitted after expiry of the deadline will not be considered in the evaluation of your application.

We offer

  • An exciting and challenging position at a developing institution
  • Position is paid and placed in position code 1017, PhD candidate in the Government Salary Scale
  • Membership in the Norwegian Public Service Pension, with among other things, good pension and insurance schemes.

For more information about INN University as an employer, please see here.

General information

For further details about the position, please contact:

HINN believes that there is strength in inclusion and diversity. We desire employees with different competencies, professional combinations, life experiences and perspectives to contribute to an even better way of solving problems. We will facilitate for employees who need assitance to realise their goals. Relevant adaptations can be, for example, technical aids, adapting furniture or adjusting routines, work tasks and working hours.

If there are qualified applicants with disabilities, gaps in the CV or immigrant background, we shall call at least one applicant in each of these categories for an interview. In order to be considered as an applicant in these groups, the applicants must meet certain requirements. You can read more on this here.

We encourage applicants to tick in Jobbnorge if they have a disability, a gap in their CV or immigrant background. The ticks in the jobseeker portal form the basis for anonymised statistics that all state-owned enterprises report in their annual reports

Information about applicants may be made public even if the applicant has asked not to be named on the list of persons who have applied. The applicant must be notified if the request to be omitted is not met.

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