Postdoc in Host-Microbiome Multi-omic Data Science

Syddansk Universitet; Odense; On-site; DKK 469,000 - 603,000; Full time

Job summary

Syddansk Universitet invites applications for a Postdoc in Host-Microbiome Multi-omic Data Science. The position is 3 years with extension possibility, focusing on research, teaching, and professional development within host-m microbiome multi-omics projects.

The successful candidate will enhance deep learning models, collaborate across international partners, and explore graph-based methodologies to interpret complex multi-omics data for health research.

Qualifications

• The candidate must have a PhD in a relevant field and strong software development experience.
• Experience with machine learning and statistical analysis is required.
• Excellent programming skills and familiarity with deep learning frameworks.

Responsibilities

• Improve and expand current deep learning approaches for multi-omics data.
• Lead and participate in data science projects within consortia and groups.
• Collaborate with clinicians, veterinarians, biologists and bioinformaticians.
• Develop novel methods including graph-based approaches like graph neural networks.

Skills

Programming (Python, PyTorch, TensorRT

Machine learning & deep learning

Team collaboration

English communication

Education

PhD in CS/Math/Physics/Bioinformatics/Biology

Tools

Python

NumPy

PyTorch

TensorFlow

Job description

Postdoc in Host-Microbiome Multi-omic Data Science

The position is limited to 3 years with the possibility of extension.

Research tasks

The position as a postdoc consists of research, teaching, professional development, and guidance in Host-Microbiome Multi-omic Data Science.

We seek a data scientist to derive insights from host-microbiome multi-omics data in multiple consortium projects studying human and animal health, including GALAXY/MicrobLiver studying liver fibrosis (https://www.sdu.dk/en/forskning/galaxy ), MICROB-PREDICT studying late-stage liver cirrhosis (https://microb-predict.eu/ ), and PIG-PARADIGM to combat antimicrobial resistance (AMR) in pig production (http://pig-paradigm.net ). We strive to integrate and interpret these data derived from microbiome (e.g., metagenomics, metatranscriptomics, viral metagenomics, metabolomics, metaproteomics) and host (e.g., genetics, proteomics, circulating metabolomics) in collaboration with international data science partners (e.g., EMBL-Heidelberg; University of California, Davis) and other project partners. The current state-of-the-art for multi-omics integration and interpretation leaves much to be desired. Fortunately, our ongoing work leveraging a deep learning approach (autoencoders) integrates multi-omics data and generates biologically relevant insights using a biologically informed machine learning framework. Our ambition is to strengthen and expand this research direction into graph-based approaches such as graph neural networks.

In this position, you will be:

Responsible for improving and expanding our current deep learning approaches, while also having a chance to create your own novel approaches.

Expected to participate in and lead existing data science projects within these consortia and the Arumugam group; and are highly encouraged to initiate new cutting-edge research projects.

Expected to interact and collaborate with other clinical researchers, veterinarians, biologists, and bioinformaticians in these consortia to enhance and ensure proper interpretation of experimental results.

Expectations of qualifications

The applicant who is hired must have a Ph.D. degree and documented research experience in one or more of the areas/fields mentioned below,with strong experience in software development.

• Computer science (informatics)
• Mathematics
• Physics
• Bioinformatics
• Biology

The Postdoc fellowship is aimed at early-career researchers with a background in basic science. We are particularly interested in candidates with strong analytical skills and interdisciplinary experience. Candidates with a solid data science background but lacking biological training are strongly encouraged to apply and include a statement about their motivation for this position and how they anticipate addressing this.

Required qualifications

• PhD in computer science (informatics), mathematics, physics, bioinformatics, or biology with strong experience in software development.
• Academic preparation as well as experience in machine learning and statistical analysis.
• Strong programming skills and experience using deep learning frameworks (e.g., Python, NumPy, PyTorch, TensorFlow).
• Experience with deep learning model architectures, including autoencoders, graph neural networks, and related neural network models.
• Familiarity with explainable AI (xAI) methods for interpreting machine learning and deep learning models.
• Experience in a high-performance computing environment using any flavor of UNIX.
• Demonstrated capacity for effective teamwork.
• Proven track record showing scientific productivity in peer-reviewed journals.
• Excellent English communication skills, both written and oral.

The following qualifications are highly preferred but not required

• Experience in applying deep learning to biological problems.
• Basic understanding of human disease biology and/or microbiology.
• Experience in analyzing metagenomic datasets from host-associated microbiota.
• Experience in handling multi-omics data, including metabolomics and proteomics.
• Experience in distributed and cloud computing technologies.
• Experience in supervising other researchers at different levels.
• International mobility.

Research strategy for OUH and the Department of Clinical Research

For further information, please contactGroup Leader Mani Arumugam, Department of Clinical Research, e-mail:[e-mail] ,phone: [phone] or Head of Department at the Department of Clinical ResearchRikke Leth-Larsen, e-mail:[e-mail] , phone: [phone]

Salary and terms of employment

The applicant will be employed in accordance withthe agreement between the Ministry of Finance and AC (the Danish Confederation of Professional Associations).

The position asPostdocis placed within salary steps 4–8 of the Danish state salary scale, depending on seniority.In addition, a centrally agreed postdoc allowance is paid, and further qualification- and function-related allowances may be negotiated.

The work location will be atCampusvej 55, 5230 Odense M.

Assessment

Shortlisting will be used as part of the initial selection process.

Assessment of shortlisted applications will be done under the existing Appointment Order for universities. Shortlisted applications will be assessed by an assessment committee. The committee may request additional information, and, if so, it is the responsibility of the applicant to provide the necessary material.

When the assessment committee has submitted its report, the applicants will receive part of the evaluation that concerns themselves.

References will be obtained for the preferred candidate from their current and previous employers. Applicants invited for interview should expect to complete a work-related personality assessment.

TheInternational Staff Office (ISO) at SDU provides a variety of services for new employees, guests and people who consider applying for a job at the University of Southern Denmark.

The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.