The PhD position is one of four PhD and post-doc positions within the UiORealArt Convergence environment at the University of Oslo. Convergence environments are interdisciplinary research groups that will aim to solve grand challenges related to health and the environment. They are funded by UiO’s interdisciplinary strategic area UiO:Life Science www.uio.no/life-science. 

The aim of this Convergence environment is to improve causal inference in perinatal pharmaco-epidemiology using machine learning approaches on real-world and artificial data” (UiORealArt, 2022-2026). The project unites expertise within natural sciences and medicine (pharmacology, machine learning, bioinformatics, statistics, genetics, and epigenetics) with social/educational sciences (psychology, language development and educational attainment).

Perinatal pharmaco-epidemiology refers to a field that studies the safety of medication use during pregnancy. Causal inference in perinatal pharmaco-epidemiology is extremely challenging because of the nature of allowed data collection procedures while the methodological progress has on some aspects stagnated. There is currently a surge of interest in applying machine learning (ML) to epidemiological questions under the assumption that it would improve causal inference. There has also been an increased interest in incorporating high-dimensional molecular data together with epidemiological data from national health registries to strengthen causal inference. However, both the application of ML and the incorporation of high-dimensional molecular data appear to have been mostly based on hype and popular trends rather than a deeper appreciation for problem characteristics and suited inductive biases.
The PhD candidate will build on the combined expertise of epidemiologists and machine learners involved in RealArt and will work at the intersection of bioinformatics, machine learning and causal inference. First, while causality has traditionally been focused on linear relations involving data of limited size/dimensionality, there is currently an increasing interest in considering higher complexity (non-linear) relations and using data of larger size and higher dimensionality. Second, while machine learning has traditionally been focused on reflecting statistical associations in fixed underlying distributions – based on a dataset assumed to be an iid representation of this underlying distribution – there has lately been an increased realization that real-world application of machine learning very often involves substantial domain shifts, and that causality provides perspectives that are very useful to reason about and to improve the generalizability/robustness of ML models in face of such challenges.

Lab website: https://phulab.org/positions

The Hu lab in the Department of Biochemistry at Western University, Canada has multiple fully funded MSc/PhD positions in artificial intelligence for bioinformatics. Western University ranks within the top 10 research-intensive universities in Canada and top 200 in the world (The Times Higher Education World University Rankings 2023). Dr. Hu is a Canada Research Chair in Computational Approaches to Health Research and has excellent experience in mentoring trainees at different levels. Many of his trainees have received international and national awards, accepted into top graduate schools (like Oxford, Cambridge, UCLA, CMU, Toronto, UBC, McGill), employed in high-tech companies (like Google, Microsoft, Amazon), and landed tenure-track professor position with nomination for Canada Research Chair. The successful candidates can be registered in Collaborative Specialization in Machine Learning in Health and Biomedical Sciences (uwo.ca/sci/datascience/graduate/collaborative-specialization-ml-health-biomedical.html) in the Hu lab through the Department of Biochemistry.

Position Summary

The Lunenfeld-Tanenbaum Research Institute (LTRI) of Sinai Health System, a University of Toronto affiliated research centre, is seeking an emerging leader in the broad area of Data Science and Health Research. The appointment will be for a Principal Investigator, rank equivalent to Assistant Professor, with the anticipated starting date of September 1, 2023.  We seek applicants that will develop independent, outstanding and innovative programs in Data Science with a specific focus on applications to health research.  Topic areas include, but are not limited to: genomics, epigenomics, transcriptomics, proteomics and metabolomics, statistical genetics, epidemiology, risk prediction modeling, data linkage, bioinformatics, quantitative biomedical image analysis, machine learning analytics and artificial intelligence application in health science for a wide range of complex diseases.

The Lunenfeld-Tanenbaum Research Institute is one of Canada’s leading biomedical research institutes offering a stimulating research environment.  The successful candidate will join the LTRI whose faculty members are internationally renowned for their work in multiple areas of cutting-edge research.  With numerous discoveries in cancer, diabetes, genetic disorders, women’s and infants’ health among others, the Lunenfeld-Tanenbaum Research Institute is committed to excellence in health research and the training of young investigators.  Large-scale health data has been collected through many research initiatives and platforms.  Within the Lunenfeld-Tanenbaum Research Institute, the Prosserman Centre for Population Health Research includes a group of multi-disciplinary scientists with focus on quantitative research of complex diseases, including population and statistical genomics based on large-scale cohorts and biobank data, and multi-omics integration.  Partnering with the Data Science Institute, Dalla Lana School of Public Health, Department of Statistical Science, and Department of Computer Science at the University of Toronto, the Prosserman Centre for Population Health Research is the hub of an active research community of public health science, genomic epidemiology, omic data science and statistical methodology.

The Institute provides a research-intensive environment with modern and innovative core facilities that include next-generation sequencing, single cell platforms, proteomics, robotics and advanced cellular imaging that drive technology development and are housed in the Network Biology Collaborative Centre and the Nikon Center of Excellence. LTRI has a fully accredited Biospecimen Repository for biobanking and a designated Research Training Centre that hosts trainees across disciplines from around the world.

Lunenfeld-Tanenbaum Research Institute research groups engage in productive collaborations within the Institute and are well integrated in the larger University of Toronto community, as well as national and international collaborations. The University of Toronto has one of the most concentrated biomedical research communities in the world, including 9 academic hospital research institutes that are fully affiliated with the University, in addition to government agencies such as the Ontario Institute for Cancer Research, Public Health Ontario, Vector Institute focused on artificial intelligence, and the newly established Data Science Institute dedicated to large-scale data science. This community attracts more than $1.2 billion in annual research investment. Toronto is a vibrant, safe and multicultural city with excellent quality of life.

The long-term objective of the Centre for Oncology and Immunology is to employ novel functional screens, animal and cell models including organoids, immuno-oncology approach, as well as advanced genomic and proteomic techniques down to single cell resolution to identify novel “druggable” cancer targets for hard-to-treat malignancies. The ultimate goal is to develop therapeutics against these targets and to conduct clinical trials evaluating these drugs along with biomarkers with an eye to their eventual commercialization worldwide. Success in this venture will address pressing unmet medical needs and establish Hong Kong as a leading hub of innovative anti-cancer and immuno-oncology drug development.

Job Description & Qualification:

Applications are invited for appointment as Bioinformatician, to commence as soon as possible.

The position will focus on tumor microenvironment in GI and liver cancer under the direction of Dr. Tracy L. McGaha as part of a collaborative program between the University of Hong Kong and the University Health Network/Princess Margaret Cancer Centre (Canada). The successful candidate will work in close collaboration with the scientific and bioinformatics teams at the Centre of Oncology and Immunology and the Princess Margaret Cancer Centre to decipher myeloid/tumor interactions using high dimensional, spatially resolved approaches.

Applicants are expected to perform the following duties include (a) co-ordinating research projects and developing research methods; (b) processing clinical and investigative data; (c) statistical analysis; (d) work independently on statistical modelling, algorithm development and high-throughput sequencing data analysis; and (e) performing other tasks as assigned.

Applicants should possess a Master or PhD degree in Bioinformatics, Biostatistics, or related disciplines, preferably with relevant research experience in Cancer Biology, Bioinformatics, Immuno-oncology or Single Cell Technology (scRNAseq, sc-ATACseq, 10X genomics & illumina platform, NanoString).

Strong working knowledge of programming language (Python, Perl, C++, or R), basic data network configuration, and statistical software packages is a must. A good publication record showing competence in bioinformatics would be an advantage.

The applicants should have a good command of written and spoken English and strong communication skills, team player, self-motivated, organized, detail- minded, hardworking, willing to learn new techniques, and able to work well in a multidisciplinary team.

The appointee shall work in the Hong Kong Science and Technology Park.

Overseas graduates are encouraged to apply, the qualification awarding institution should be the top 100 institutions for STEM-related subjects in QS/Shanghai Jiao Tong Uni/Times Higher Education world university rankings.

A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.

Enquiries about the post should be sent to career@coinno.hk

How to apply:

Applicants should submit their up-to-date C.V. and research publication list, the position being applied for and information of current/expected remuneration and availability, to career@coinno.hk mentioning the McGaha lab and the reference number [Ref.: H03/2022/A4/UHN] in the subject line.

The personal data provided in your application process will be used for recruitment and other employment-related purposes. The personal data may be transferred and disclosed to, and used by HKU Innovation Holdings Limited and The University of Hong Kong for the above purposes.

We are an equal opportunity employer and welcome applications from all qualified candidates.

Summary of the research project

The objective of this thesis is to propose a methodology to select and measure in a precise, simple and repeatable way the phenotype of Tahitian pearls on a few thousand individuals per lineage and per year, if possible in a non-lethal way on the candidates (or lethal on parent oysters that were used as genetic material in the transplantation process). The pearl phenotypes are color, luster, orientation (iridescence), aragonite deposition rate, surface and shape defects, and nacre thickness.

The developed phenotyping method will be tested on a sample of 200 to 500 pearls representative of the different existing pearl classes. Once validated, our methodology will be used to perform a genetic selection of oysters to improve the quality of the pearls produced.

This project will be carried out in partnership with the French Polynesian Marine Resources Department and local pearl farmers.

General context of the project

The Tahitian pearl is a naturally colored cultured pearl that comes from the grafting and rearing in a natural environment of the pearl oyster Pinctada margaritifera. The pearl consists of a deposit of layers of mother-of-pearl (aragonite platelets) around a nucleus, an artificial nucleus inserted into the oyster by a grafter. The Tahitian pearl enjoys an excellent reputation for quality on the international market and is a benchmark against which all pearl production throughout the world is compared.

The quality and beauty of a pearl depends on a large number of criteria: the thickness of its nacre, its shape, the quality of its surface, its color and its luster. An international codification and a local codification in Tahiti (defined in 2001) evaluate the quality of pearls according to these criteria. Today, the evaluation of these criteria is generally done by experts.

The process is by no means automatic.We already obtained some results in the automatic evaluation of some of these criteria:

• We proposed a method for the automatic measurement of the thickness of the nacre of Tahitian pearls, for which we have filed a patent
• We also defined a method for classifying the luster of Tahitian pearls.
• We also initiated some works on the classification of the color set of a Tahitian pearl.

The main objective of this PhD thesis is to build on the results already obtained by completing them with respect to quality criteria that have not yet been fully studied, such as color, defects and shape, in order to move towards a complete automatic phenotyping of a Tahitian pearl.

The results obtained will make it possible to draw up an identity card of a pearl and its quality on all the assessable points.