Land a University of Michigan SEAS Postdoc in Environmental Economics 2026: Build Causal Evidence on Climate, Air Pollution, and Informal Industry

Some postdocs are basically “keep writing papers and try not to spill coffee on the departmental carpet.” This one is not that.

The University of Michigan School for Environment and Sustainability (SEAS) is hiring a Postdoctoral Research Associate to work with Dr. Nina Brooks on research that sits right where the big questions collide: environment + development + health economics + policy. If your happiest place is somewhere between a clean identification strategy and a messy real-world dataset, keep reading.

The intellectual hook here is unusually strong. You’ll be connecting survey, field-based, and administrative data to high-resolution climate and air pollution datasets—the kind of work that turns vague conversations about “climate impacts” into numbers that policymakers can’t ignore. The program’s particular emphasis on informal brick manufacturing and other informal industries is also a not-so-subtle signal: this isn’t research for research’s sake. It’s research that points at real exposure, real labor conditions, and real health outcomes.

And yes, it’s a postdoc, so the publication engine matters. But what makes this opportunity special is the mix: you’re expected to contribute to ongoing projects and pursue your own related research agenda. That’s the sweet spot most postdocs advertise and fewer actually deliver.

The deadline is February 28, 2026. If you’re serious, you should already be thinking about your cover letter like it’s your opening statement in a court case: clear, specific, and impossible to ignore.

At a Glance: University of Michigan SEAS Postdoctoral Research Associate 2026

What This Postdoc Actually Offers (Beyond a Line on Your CV)

Let’s talk about the real package here, because “postdoc” can mean anything from “independent scholar with resources” to “data gremlin in the basement.” This one reads like the first kind—demanding, yes, but intellectually meaty and professionally useful.

You get a serious research sandbox

You’ll be working with a blend of data sources that don’t naturally play nicely together: surveys, field measurements, administrative records, and remote sensing products like temperature and air pollution. If you’re the kind of researcher who gets excited by merging datasets across space and time (and can tolerate the pain that comes with it), you’ll have plenty to do.

The emphasis on high-resolution climate and air pollution datasets matters because it pushes your work toward stronger measurement and sharper inference. It’s one thing to say “hotter days reduce productivity.” It’s another to show it using granular exposure measures matched to individuals, firms, or worksites, with a credible strategy for causality.

You sharpen (and prove) causal chops

The role explicitly calls for econometric analyses to estimate causal effects and mentions randomized controlled experiments. Translation: your methods section can’t be decorative. You’ll be expected to know why your identification works, defend it, and implement it cleanly.

If you’re building a career in applied micro, development, environmental econ, health econ, or policy evaluation, this kind of portfolio is gold—because hiring committees don’t just want “interesting topics,” they want evidence you can produce results that survive a skeptical seminar room.

You’re not boxed into one narrow question

A lot of postdocs quietly discourage independent work. Here, the posting is unusually explicit that you’ll pursue your own related research interests while contributing to the lab’s projects. That creates a path to leave the postdoc with a coherent job market paper pipeline (or at least multiple strong drafts), not just coauthor slots.

Benefits that make life less chaotic

University of Michigan offers a comprehensive benefits package including time off, retirement with two-for-one matching contributions and immediate vesting, health insurance options, plus protections like life insurance and long-term disability, and flexible spending accounts. That might sound mundane until you’ve lived the postdoc life with flimsy coverage and a budget held together by optimism.

Who Should Apply (And Who Should Probably Not)

This postdoc is a strong fit for someone who already thinks like an applied empirical researcher and wants to aim that toolkit at environmental exposures in lower-resource settings and informal industry contexts.

If you have (or will soon have) a PhD in Economics, Public Policy, Data Science, or a closely related quantitative social science, you’re in the target zone. The posting allows you to be finishing up—as long as your PhD is completed within four months of the start date. That’s helpful if you’re currently in the “I’m 80% done but my dissertation is acting possessed” phase.

You should feel genuinely comfortable with causal inference. Not “I took a metrics sequence once.” More like: you can explain the assumptions behind difference-in-differences, instrumental variables, matching, synthetic controls, or panel strategies without looking like you’re reading from a teleprompter. If you’ve done serious work in RCT design/analysis, even better.

On the computational side, they’re clearly not looking for someone who fears the command line. They want strong proficiency in R, comfort with version control (Git/GitHub), and familiarity with high-performance computing (HPC). Think of this as a role for someone who can keep their analysis reproducible and scalable—because the datasets can get big fast.

Finally, the topic area matters. The emphasis on informal brick manufacturing and informal industries suggests exposure, labor conditions, and health outcomes in complex settings. If you’ve worked on development contexts, labor econ in informal sectors, environmental health, climate impacts, or policy evaluation tied to exposure reduction, your experience will translate well.

Who might struggle? If your background is purely theoretical, if you dislike cleaning messy datasets, or if you strongly prefer small-N qualitative work with minimal quantitative components, this is probably not your best match. This job rewards people who can turn chaos into credible estimates.

What You Will Likely Work On (Based on the Posting, Plain English Edition)

The responsibilities listed are direct, and they paint a clear picture of the day-to-day:

You’ll integrate human data (survey/admin) with environmental exposure data (temperature, air pollution) from remote sensing or monitoring. That means doing careful spatial joins, time alignment, and exposure assignment—work that is both technically finicky and scientifically important.

You’ll run econometric models aimed at causal interpretation. This is where you’ll need to think hard about confounding, selection, measurement error, and policy endogeneity. In other words: you’re not just estimating relationships; you’re trying to identify effects.

You’ll support the design and analysis of randomized controlled experiments, which could include everything from power calculations and randomization protocols to pre-analysis plans and treatment effect estimation.

And you’ll write. A lot. The role expects you to lead manuscript preparation and help disseminate findings. In practice, that means you should enjoy turning results into clean tables, sharp figures, and arguments that make reviewers stop sharpening their knives.

Insider Tips for a Winning Application (The Stuff People Learn the Hard Way)

This is a tough postdoc to get, but absolutely worth serious effort. Here’s how to move from “qualified” to “obvious hire.”

1) Treat the cover letter like a research memo, not a biography

They explicitly require a cover letter and want it attached as the first page of your CV. Follow that instruction exactly. Then write the letter as if you’re answering one question: Why you, for this work, right now?

A strong structure is simple:

• Your research identity in one paragraph (topics + methods).
• Your fit for their agenda (environment-development-health-policy intersection; informal industry; exposure data).
• Your technical toolkit (R, Git, HPC, remote sensing integration) with proof points.
• What you’d pursue independently during the postdoc (1–2 concrete project ideas that align).

2) Name your causal inference strengths with receipts

Don’t just say “experienced in causal inference.” Give examples:

• “I implemented staggered-adoption DiD with event studies and sensitivity checks.”
• “I used IV with a clear first stage and defended exclusion restrictions in writing.”
• “I pre-registered an RCT analysis plan and handled attrition and multiple hypothesis testing.”

Make it easy for them to imagine you producing credible results quickly.

3) Show you can handle environmental exposure data without panicking

If you’ve worked with remotely sensed PM2.5, NO₂, aerosol optical depth, land surface temperature, reanalysis products, or satellite-derived measures, say so. If not, show adjacent competence: geospatial work, raster operations, exposure assignment workflows, validation, or measurement error handling.

This posting screams: “We have rich data; we need someone who won’t break it.”

4) Your writing sample should match the job you want, not the job you had

Pick a sample that demonstrates:

• Clear identification strategy
• Strong data work (even if you can’t share the raw data)
• Policy relevance
• Clean, readable writing

A dense methods appendix with no narrative won’t help you. Neither will a purely descriptive paper if the role is causal-heavy.

5) Use your references strategically (and brief them like adults)

They ask for contact info for at least three references. Choose people who can speak to different strengths:

• Causal inference and econometrics credibility
• Data/scaling/reproducibility competence
• Collaboration and project leadership

Then send them the posting, your cover letter draft, and a short paragraph on the projects you expect to do in the role. References write better letters when you give them ammunition.

6) Demonstrate you can collaborate across disciplines and borders

The posting highlights interdisciplinary and international teamwork, plus possible South Asia fieldwork. If you’ve collaborated with public health, environmental science, or policy teams—or worked with partners outside your home institution—spell it out. Mention how you handled communication, data sharing, and timelines.

7) Signal maturity about messy realities

Research on informal industries can involve irregular records, missing data, measurement challenges, and field constraints. Without being dramatic, show you’re realistic: you anticipate problems, you plan robustness checks, and you don’t crumble when Plan A collapses.

Application Timeline: A Realistic Plan Backward From February 28, 2026

If you start two weeks before the deadline, you’ll submit something technically complete and strategically weak. Give yourself room to be thoughtful.

8–10 weeks before (early January 2026): Decide on your writing sample and update it. Draft the cover letter with specific alignment to the lab’s focus (exposure + informal industry + causal inference). Identify references and ask early—academics are busy, and “urgent” emails magically become invisible.

6–7 weeks before: Polish your CV and ensure the cover letter will be attached as page one. Tighten your research narrative so it’s consistent across CV, letter, and sample. If your writing sample is coauthored, prepare a short note clarifying your contribution (even if not required, it’s often helpful).

4–5 weeks before: Do a ruthless clarity pass. Make sure a reader outside your subfield can understand (a) the question, (b) the identification strategy, and (c) why it matters. If they can’t, revise.

2–3 weeks before: Confirm references are ready and have the correct submission details. Double-check formatting and upload requirements in the careers portal.

Final week: Submit early. Career portals have a talent for failing at the worst possible time, and “the website crashed” is not a persuasive argument after the deadline.

Required Materials (And How to Make Each One Pull Its Weight)

The application is refreshingly focused, but that doesn’t mean it’s easy.

• Cover letter (required): Attach it as the first page of your CV, exactly as instructed. Make it specific to SEAS, Dr. Brooks, and the stated research themes. Generic letters read generic.
• Curriculum Vitae (CV): Make sure it highlights empirical work, datasets, methods, and publications/working papers. Put software skills somewhere visible, not buried.
• Writing sample: Choose your strongest causal paper. If the full version is long, consider a clean working paper draft rather than a messy dissertation chapter full of placeholders.
• Contact information for at least three references: Provide accurate titles, emails, and institutional affiliations. Then make sure those people actually expect to be contacted.

What Makes an Application Stand Out (What Reviewers Are Really Scoring)

For a role like this, the “scorecard” is pretty predictable.

First, methodological credibility. They need someone who can run causal analyses that won’t collapse under basic robustness questions. Show that you think like an applied econometrician, not just a software operator.

Second, data competence at scale. Integrating administrative/survey data with remote sensing is not a weekend hobby. Evidence that you can build reproducible pipelines, manage large data, and use version control will put you ahead.

Third, topic alignment with real curiosity. The best candidates won’t just say “I care about climate and health.” They’ll show a sustained interest in how environmental exposures translate into economic and health outcomes, especially in informal or understudied settings.

Fourth, writing and dissemination ability. A postdoc who can’t publish is like a chef who can’t taste. Your writing sample is doing a lot of work here.

Common Mistakes to Avoid (And How to Fix Them)

Mistake 1: Writing a cover letter that could fit 50 other postdocs

Fix: Mention the specific intersection the job emphasizes—environment, development, and health economics/policy—and connect it to your actual work, not your aspirations.

Mistake 2: Being vague about skills like R, Git, or HPC

Fix: Include one or two concrete examples. “Built an R pipeline for raster-based exposure assignment and managed reproducibility with GitHub” beats “proficient in R.”

Mistake 3: Submitting a writing sample that hides your identification strategy

Fix: Pick something where the causal logic is upfront, readable, and defensible. If the identification is buried, reviewers may assume it’s weak.

Mistake 4: Overpromising independence without showing you can collaborate

Fix: Balance “I have my own agenda” with “I work well on teams.” Cite coauthored work, interdisciplinary projects, or collaborative data builds.

Mistake 5: Ignoring the informal industry emphasis

Fix: Even if you haven’t studied brick manufacturing, show you understand informal sector dynamics and measurement challenges. Demonstrate you’re not romantically naïve about data constraints.

Frequently Asked Questions

1) Is this a fellowship or a job?

It’s a postdoctoral research associate position, meaning a paid academic appointment rather than a traditional “apply-for-money” grant. You apply through the University of Michigan careers portal.

2) Do I need the PhD in hand by the deadline?

The posting indicates you must have the PhD before appointment or within four months of the start date. You can apply while finishing, as long as your completion timeline is realistic.

3) What disciplines are eligible?

They’re looking for quantitative social science training—explicitly Economics, Public Policy, Data Science, or a closely related field. If your PhD is in a neighboring discipline but your methods match (causal inference, applied metrics), you can still be competitive.

4) What technical skills are non-negotiable?

Expect R, Git/GitHub, experience with large-scale environmental/remotely sensed data, and some familiarity with HPC to be core expectations, not “nice-to-haves.”

5) Is fieldwork required?

Not necessarily. The posting notes opportunities for field research in South Asia depending on your interests. If you love fieldwork, say so. If you prefer to stay data-focused, you can still be a fit if your skills align.

6) What should I write about in the cover letter?

Write about (a) why this specific research area fits your trajectory, (b) what you bring methodologically, (c) your experience with relevant data, and (d) what you’d work on during the postdoc—ideally in a way that complements ongoing projects.

7) Can I submit more than one writing sample?

The posting asks for “a writing sample” (singular). Unless the portal allows multiple uploads and instructions explicitly permit it, stick to one strong sample and make it count.

8) How “policy” does this role get?

The work lives at the intersection of economics and policy, so the best applications will show you can connect empirical results to real decisions—regulation, enforcement, exposure reduction, labor protections—without turning your discussion section into a soapbox.

How to Apply (Do This, Not That)

Start by preparing your cover letter and CV as a single combined document with the cover letter as page one, because they explicitly request that format. Save it as a clean PDF with a sensible filename (no “final_final_reallyfinal3.pdf” energy).

Choose your writing sample with intention. You’re auditioning for a role that values causal inference, environmental exposure data integration, and clear scholarly communication. Make the sample prove you can do those things.

Line up three references who can speak to your methods, data competence, and collaboration style. Then warn them that they may be contacted and give them context so they can write or respond intelligently.

Finally, submit early enough that you can handle portal glitches without sweating through your shirt.

Ready to apply? Visit the official opportunity page here: https://careers.umich.edu/job_detail/apply/272587