Opportunities for motivated and talented students are often available (or can be created). We have a highly multidisciplinary team and aim to recruit prospective students and collaborators from various field of study, including (but not limited to) math, computer science, statistics, demography, epidemiology, engineering, physiology, ecology, evolutionary biology, sociology, economics, and biochemistry. All students are expected to have substantial quantitative skills or the capacity to learn them easily, but this can take various forms (statistics, data mining, mathematical modeling, computer simulations, etc.)
Students in my lab can participate in a number of graduate programs at the University of Sherbrooke, including Health Sciences, Physiology, Biochemistry, Mathematics, or Ecology and Evolution. As our work does not fit neatly into any of these programs, I will work with you to help find a program that is appropriate for your interests and career goals.
Though informal inquiries are encouraged, we expect interested applicants to prove their seriousness by becoming familiar with our research (at a minimum reading through this website and a few articles) before contacting us. Please make sure that your reasons to continue to graduate studies are the right ones (see below) and that your interest in our research is sincere. Starting a master’s or a doctoral program is a serious process that should not be taken lightly or chosen simply to avoid facing the labour market. Lack of motivation is the main cause of failure in graduate studies. Prospective students should refer to our lab philosophy, policies and objectives to see if it meets their expectations. Student inquiries regarding supervision should include the following:
- a CV;
- contact information for at least 2-3 references;
- a letter stating your general interest in our research and why you chose me as a supervisor.
Living in Sherbrooke is cheaper than in most other Canadian cities. In Quebec, you can expect a minimal annual salary of $17,500 for a master degree and of $19,000 for a PhD degree, and research awards for graduate studies and fellowships are mainly provided by the CIHR (Canadian Institutes of Health Research) and the FRQ-S (Fonds de recherche du Québec – Santé). More information about graduate studies at Sherbrooke can be found on the university web page. Please make sure to contact us well before your anticipated start date, so that all necessary arrangements can be planned ahead.
Please note that Sherbrooke is mainly a French-speaking town. Even though all the members of our lab are bilingual, lab meetings are usually held in French. We do not reject candidates who do not speak French, but an interest to learn the language is considered an asset.
What to expect as a student in my lab
Before considering graduate or post-doctoral studies with me, please read our lab principles section carefully. I have also compiled some pros and cons of pursuing a PhD: in my experience, most people start a Ph.D without knowing what they are getting into, and this will help orient you as to what to expect more broadly than just in my lab.
Research skills to acquire with an advanced degree
An advanced degree is a process of acquiring a variety of specific knowledge and skills. Much of what you will acquire is specific to your project and field, but there are some general skills that all students in my lab are expected to develop. It is your responsibility to develop these skills on your own, in addition to the formal ways I work with you. Here are some of the key skills:
You need to learn how to search online databases of scientific publications and find publications relevant to a certain subject or question. You need to learn how to read large numbers of articles quickly, extracting the key information from each article and making judicious choices about when to read in more detail. It is important also to view articles not as independent entities, but as contributions from a given lab, researcher, or collaborative group. This helps put them in a larger context and identify potential biases.
You will need to perform a broad literature review around your principal topic of interest that will sometimes lead you far afield. For example, my Ph.D thesis was on how circulating antioxidant levels in wild birds might be related to the evolution of lifespan. In the process, I became relatively expert in the following fields (much of which I have since forgotten): (1) Antioxidant biochemistry; (2) Measurement techniques for antioxidants in blood and tissue samples; (3) Biological mechanisms of aging; (4) Comparative biology of aging; (5) Avian ecology; (6) Techniques for taking and storing blood samples from wild birds, and biases in that process; (7) Evolutionary biology and techniques like phylogenetic analysis; (8) Basic and mid-level multivariate statistics; (9) Data management and cleaning; (10) Nutrition and micronutrients. This list is likely far from exhaustive, and gives a sense of how widely you will need to read on your chosen topic.
This is the key to being a good scientist. Without this, all your efforts are for naught.
This step in research is often forgotten, but is critically important. What do you do with a very strange value, such as a human height of 2.3m? What about missing data? How are the data organized to facilitate analysis? How are different versions of the data stored, and can the steps of management be retraced if an error is found or a different cleaning procedure is desired?
Obviously, this is at the core of much of our lab’s research, and we will devote substantial time working on this. The most important principle is that rules are not written in stone, and you need to think through what each analysis means in the context of your question and the possible biases.
Once the analyses are done, the key results need to be communicated clearly, usually through tables and figures. Arriving at the right presentation format is essential to convey your findings well, and is an art in itself.
This is one of the hardest tasks for students, and your first article will likely require many revisions before it can be submitted. In most cases, it would be much faster for me to just write the article for you – the goal of the process of revisions is thus for you to learn how to write, and you need to read my comments carefully as part of a learning process, not just changes to be implemented. Critical issues to consider are audience (who will read this, and what do they already know?), content (what information is needed and what is not?), structure (what information goes where to ensure a logical flow?), angle (the same data can be presented many ways; what is the best way to convey the key findings?), and sales (publishing a scientific article is an exercise in marketing it to reviewers, editors, and readers, and this needs to be considered, distasteful as it may be).
You will need to be confident presenting your work and discussing it broadly with colleagues, and to be able to arrive at a logical structure that takes into account the background needed for a given audience.
If you don’t put sufficient attention into the details of your research, you will make big mistakes. But if you lose the forest for the trees, you will never accomplish anything. You will also likely have other tasks in life, and your thesis is unlikely to be pressing (rarely a deadline tomorrow). You will need to find the right balance in all these things to advance well.
Whatever your research type (bench, field, statistics), you will need to keep careful track of all the steps of your research and be able to retrace your process. This is essential for finding errors/anomalies, but also for eventually writing methods and reproducing results. You will need to figure out how to be organized.
None of us can be sufficiently expert in all fields to succeed without collaboration, but you also need to be able to work independently. You will need to learn to motivate yourself, make your own schedule, develop your own ideas and approaches. You will also need to learn when to ask for help and who to ask for it (hint: not always me). You will need to learn to draw on strengths of different people inside and outside the lab, and to work collaboratively when appropriate.
Most research involves some ethical considerations (treatment of animals, human experimentation, confidentiality of health data, etc.). Because we work largely with existing data sets, these issues often concern our lab less than some others. Nonetheless, you will need to consider ethics in research and the importance of maintaining high standards. One small error that gets publicized can have grave impacts on the whole research community. You will also need to consider ethical issues surrounding honesty in publishing and the tension between advancing one’s career and doing the best science. There are many difficult moral questions that arise daily in research, and now is the time to start considering them.
Like a traffic system or an ecological system, science itself is a system. Just as congestion, traffic lights, and road patterns affect the routes people take, their commuting times, and the efficiency of transport, in science, career opportunities and incentives structure how science gets done, what questions are asked, and how valid results are. People are drawn toward careers in science by factors such as salary, job availability, and interest. Grants are funded by organizations, public or private, and these organizations impose specific rules. Publications happen in journals through a system of peer-review. The small details of this system have major impacts on how science works and how effective it is at producing new knowledge. They also affect potential biases. You need to be thinking about the system of science and how it impacts everything from your project choice to your results to your career. You need to learn not just that science is a system, but also the details of how that system works. What are reviewers looking for in a manuscript? What kinds of career paths are available? What achievements are necessary to pursue different careers? What is required to succeed in a grant competition? You must not become so expert in working the system that you lose sight of why you are here, but you cannot ignore the system if you wish to pursue a career in it.