People
Mentoring in the Fritsche Lab
Mentoring in the lab means working through research questions together, learning from one another, and growing more confident in your own work.
Growing as a researcher
People join the lab with different skills and experience. Lars works with trainees on their research questions, study design, and analyses. As you gain experience, the aim is to help you take more ownership of the work and feel confident explaining the choices you make.
Regular one-to-one meetings are a chance to talk about how your project is going and where you could use help. You can bring a rough idea, a puzzling result, or a piece of code you’re stuck on. We use these conversations to work through feedback and decide what to try next.
Lars has mentored and co-mentored master’s and doctoral students, postdocs, and early-career faculty. Many of these collaborations have led to joint publications. Postdocs can also gain experience mentoring others when a suitable project and support are available, for example through the University of Michigan’s Undergraduate Research Opportunity Program (UROP).
Learning together
Our members work on different projects and with different teams. These regular conversations bring us together to share what we’re learning and support one another.
Some lab members work fully remotely from within the US; others work a hybrid schedule. Meetings usually take place on Zoom, and we use Slack for questions, updates, and sharing resources between meetings.
We meet as a lab about every two weeks to discuss science, hear seminars, and explore topics that interest the group. Short project updates help us keep up with one another’s work and find connections that could lead to collaborations.
We encourage lab members to share useful tools and lead short, practical sessions. Topics may include working with AI coding agents or running analyses on computing clusters with SLURM. We share some of these materials publicly, including the SLURM Playbook and the AI guides below.
Learning to use AI thoughtfully
Our shared training includes learning to use AI in research and coding. We draw on the University of Michigan’s GenAI resources and training and guidance for AI in research.
Our Practical GenAI Coding Guide (Part 1) introduces planning, prompting, reviewing, and documenting code. The companion Practical GenAI Agentic Coding Guide (Part 2) puts those habits into practice in a Python project with synthetic data. Its six lessons follow a change from the initial request through testing, review, and handoff, with a 45-minute workshop for shared learning.
We review and test AI-generated code, check claims and sources, and document AI use as required by the project, funder, or publication. We remain responsible for the analyses and conclusions. Public or synthetic examples are useful for learning; sensitive research data belong in services and workflows approved for those data.
Working well together
Many of our projects bring together people with backgrounds in statistics, computing, and clinical research. We take time to understand each other’s questions and explain our methods. Asking for help and giving others credit are part of working well together.
Brief shared notes help us pick up where we left off: what we discussed, why we made a decision, and what comes next, including who will follow up. They’re especially useful when people work across several projects.
Share your work the way you’d want to receive it.
When sharing code, data, or summary statistics, help the next person understand and use them. Explain the results, define columns and units, and include the code and a short README. For spreadsheets, describe each sheet and how the numbers were calculated.
Work in progress is welcome. Say what you’ve assumed, what you’re unsure about, and where you need help. Ask early if something you receive is unclear, and acknowledge the care others put into making their work useful.
Opportunities to join us
We currently mentor trainees and scholars through K12 career development programs, T90 research training programs, and funded research projects. Opportunities are limited by available funding, suitable projects, and the time we can devote to mentoring.
We’ll advertise future positions in our Opportunities section. You can also find our current recruitment status and links to graduate admissions information there. For research collaborations, see ways to work with us.