Postdoc hiring season at Flatiron

Every year, in the Center for Computational Mathematics at Flatiron Institute, we hire 7 to 10 postdocs to keep up our steady state of roughly 20 postdocs (more and more of them are leaving after 1–2 years to go to industry). Here’s the job ad for this year, with applications due on the absurdly early date of November 15, 2026 (we’re still playing catch-up with plans that make postdoc offers early December):

The 12-month salary is US$95K plus a US$10K research budget, way better compute resources than most universities have (e.g., hundreds of H100 GPUs which you can actually use), and exceptional benefits (not only free lunch and breakfast, but also great medical care and retirement plans, which has extreme variance among employers in the US). The positions are three years (technically two, with a one year renewal, but nobody has ever been denied a renewal in the 6 years I’ve been here).

Our center is split into three primary research areas, each of which has a web page describing the people and research areas:

I’m here, as are Steve Bronder and Brian Ward, two software engineers who spend a lot of their time working on Stan, along with other great software engineers (like Jeff Soules and Jeremy Magland who built the Stan Playground with Brian and also MCMCMonitor), and perhaps most importantly, a really engaged and lively group of postdocs working on inference (largely from a diffusion or normalizing flow perspective). This place is great for collaboration both internally and externally.

The postdocs are really fellowships—it’s not like American academia where I get a grant and hire you to work on the grant. You can really work on whatever you want that’s on mission as a postdoc here. So let me recall our mission, which actually matches what we do:


The mission of the Flatiron Institute is to advance scientific research through computational methods, including data analysis, theory, modeling and simulation.

Of all the places I’ve worked over the last 40 years, Flatiron is by far the best. There’s not even a close second. And I’ve been lucky in having great jobs with top notch colleagues (prof at CMU, researcher at Bell Labs, industrial researcher and software engineer writing production code, research scientist at Columbia, then here).

We’re also in a great location—the Flatiron neighborhood of New York City, which is a 10 minute walk to NYU, 10 minute walk to Google and Meta, and 20 minute subway ride to Columbia. Baruch College, the CUNY graduate center, and Fordham’s Manhattan campus are all nearby, and Yale, Rutgers, and Princeton are all in day-trip distance with lots of collaboration.

We’ve had great postdocs, so perhaps not surprising they’ve gotten good jobs: industrial ML jobs at Anthropic, OpenAI, Nvidia, and DataBricks, and stats faculty jobs at Duke University, Johns Hopkins University, the University of Texas, and the University of British Columbia. And that’s just the ML/stats side of our postdocs.

If you want pretty pictures of our setting, check out my job ad from 2021.

If you’re going to be applying in the area of Bayesian modeling or inference, please contact me directly so I don’t miss your application (we got nearly 300 applications for postdocs last year!): [email protected].

Postdoc and doctoral student positions in Bayesian workflow at Aalto, Finland

This job ad is by Aki

I’m looking for postdocs and doctoral students to work on Bayesian workflow. The candidates need to have knowledge of Bayesian inference and some experience with building models (for real applications, as part of methods development, or as part of courses). Although we have published Bayesian workflow book, there is still a lot more to do. The focus in the group is in cross-validation, model checking and inference diagnostics (see my publication list).

All positions are fully funded and the salaries at Aalto CS are 53k€-55k€ / year for postdocs and 40k€-45k€ / year for doctoral students. There are occupational healthcare and other benefits. Postdoc positions are typically offered for up to three years and doctoral student positions for four years. Starting dates are flexible and the details of each position will be agreed individually.

You can apply via joint ELLIS Institute Finland call and pick me as your preferred supervisor.

Ph.D. student opening in Sweden on Earth Observation, Data Science, and AI for poverty estimation

Adel Daoud writes:

I’m writing to ask for your help circulating a PhD opening in my group at Chalmers, the AI and Global Development Lab (www.aidevlab.org). The position is in Earth Observation, Data Science, and AI for poverty estimation, the Data Science and AI division (Department of Computer Science and Engineering). We are looking for candidates with a strong grounding in data science, computer science, deep learning, statistics, or similar— remote sensing experience and causal inference are welcome bonus.

Ad and application portal: https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job&rmjob=14818&rmlang=UK
Deadline: 20 June 2026.

Here’s the description of their center:

The AI & Global Development Lab fuses AI with Earth Observation to illuminate the causes and consequences of human development across time and space.

Our interdisciplinary team, comprising data scientists, computer scientists, and social scientists, develops methods to better understand the multi-scale dynamics of pressing global issues, including poverty, conflict, sustainability, and the effectiveness of policy interventions.

By analyzing satellite imagery from 1984 to the present, AI search agent swarms for large-scale knowledge discovery, and other planetary-scale sources, we are reconstructing historical and geographical development trajectories at a level of detail never before possible, working to offer new insights into the changing face of development worldwide.

We also invite you to visit PlanetaryCausalInference.org for more information about the causal arm of our project.

They call it “Planetary causal inference,” which seems to fit the themes of this blog.

Survey Statistics: Blue Rose Research is (still) hiring !

As readers may know, I’m a survey statistician at Blue Rose Research. We survey the public to forecast elections and test political messages, used to advise Democrats. On this blog we’ve discussed our 2024 election retrospective and announced hiring (April 2025 and October 2025). And now we hiring again !

We are hiring a Machine Learning – Scientist (salary: $140k – $190k). This will be with our forecasting team, who track public opinion, forecast election results, develop resource allocation tools, and deliver polling analysis that clients rely on to set strategy. We’ve built a technical stack that enables cutting-edge statistical, machine learning, and engineering solutions.

Any additional roles we open up will be updated here. There’s an option to sign up for alerts for new roles via email if you’d like.

All positions are remote, with an option to be in person in New York City.

Please circulate and apply !

My (uninformed and completely speculative) theory about Jeff Bezos and the Washington Post

Someone pointed me to this post from former Washington Post columnist Philip Bump:

The link to Bump’s longer post is here. I’m not questioning Moynihan’s or Bump’s numbers–I haven’t checked them myself, but I have no reason to believe that they’re wrong–but I think they’re both kinda missing the point regarding Bezos’s motivations.

Here’s my story (which, as noted in the above title, is entirely speculation):

Jeff Bezos is a rich guy with money to spare. He can afford to buy a major newspaper if it is for sale, or to start his own newspaper if he wants to do so. He could’ve done this in Seattle, for example. But that’s not just true of Bezos, it’s true of many other rich people, and most of them are not buying or starting up news organizations. There are reasons for this: first, running a newspaper costs money, and even if you’re rich, you don’t want to throw money away. Second, fewer and fewer people read newspapers or even watch TV news. If you buy or start a newspaper, you’re not getting in on the ground floor; it’s more like you’re stepping into an elevator that’s in free fall. The dream in tech is to find the next big thing with unlimited potential, not to invest in a mature and declining industry.

Why, then, did Bezos buy the Washington Post in the first place? Well, it is a unique property with influence and a storied history, so maybe it’s just that it became available and he grabbed it. He might have had some vague goal of supporting journalism, also owning a newspaper provides some protection against biased reporting. Even if Bezos is owning the newspaper in a hands-off way, and its reporters are willing to report bad news against him, presumably they’ll give him a fair shake and not be actively biased against him. Later he was hassled by the National Enquirer, so it’s not like this sort of thing couldn’t happen. If you think the media might do you harm, it can be good to have your own media outlet. So, some mix of public spiritedness, the idea of being a leading citizen, also with possible defensive value. And, who knows, maybe the possibility of a business success.

There’s a logic to it. Supposing you can afford the initial outlay and the ongoing cost, if you buy or start a newspaper, staff it with serious journalists and editors, and let them do their thing, you’ll have bought yourself some good press and some protection against biased reporting and political intimidation, you can feel you’re making some contribution to civil society, and it’s a toy to play with; nothing wrong with that!

But then what happened between 2013 and 2026? My guess is that it all just became too much hassle. Instead of being a source of good press, owning the Washington Post became a public relations hassle. When it reports stories or run op-eds that make Republicans look bad, Bezos gets attacked from the right. When he tells his staff to go easy on Republicans, he gets attacked from the left. Dude’s getting slammed from both sides. If he lets the Post continue to be run by its editors, he gets no credit from the center or left but he has to deal with an ongoing stream of annoyance from the right. But when he decides to solve that problem by bringing in a compliant outside editorial team, he’s suddenly the man who killed the news.

Meanwhile, it’s not clear what benefit Bezos is getting from owning the newspaper. In theory he could use his ownership of the Post as a political tool, for example telling Trump that if the government doesn’t give Amazon some juicy contract, he’ll start filling up the front pages with Jeffrey Epstein stories. But in practice it doesn’t seem that this is the sort of hardball that Bezos likes to play. Avoiding sales tax is one thing, lobbying is fine, but maybe outright threats would be an escalation too far.

So, instead of being fun, good publicity, and a sort of political insurance, owning the Post has become the opposite: it’s a pain in the ass, invites attacks form all sides, and entangles him in politics more than ever. Also it keeps draining money.

So, from that perspective, it makes sense for Bezos to wash his hands of the whole thing. The point is not the cost of running the newspaper as compared to his unimaginable wealth is irrelevant.

At this point you might wonder why Bezos doesn’t sell what remains of the newspaper or just shut it down. It could be a business decision on his part, or maybe an assessment that it could be useful to have the Washington Post around if you’re in some fight involving public opinion.

And, again Bezos isn’t the only zillionaire out there who can afford to buy a big-city newspaper. The fact that zillionaires (whether politically-minded or just motivated to protect their business interests) are not queuing to buy newspapers or set up their own alternatives suggests that they don’t see much value in media properties. There’s also something about the nature of the business: buy a news or organizations and it comes with all these reporters who want to report the news without political interference, then you have to suck it up and get bitten by the people you’re feeding, or you have to fire a bunch of people and replace them with compliant substitutes. Maybe this will be less of an issue going forward as the supply of conservative journalists increases.

P.S. Sam in comments offers another plausible explanation, which is that Bezos saw this as a business opportunity and thought that he’d be able to turn the Washington Post into a profitable newspaper.

Looking for a postdoc to teach and develop Bayesian methods

This job ad is by Aki

I’m looking for a postdoc to help organize Bayesian Data Analysis course (200 students) and to do research on Bayesian workflow at Aalto University, Finland. Background in Bayesian topics needed. Up to five year contract possible.

Job is at Aalto University, Espoo (15min Metro ride from Helsinki center), Finland.

Starting time is flexible. There is no specific deadline, but it is better to apply soon. Apply by email (see contact information). Include CV and explanation of your skills and experience related to teaching and research.

Salary and occupational benefits are better than in academia in many other countries, and living costs are moderate (not the cheapest country but also less expensive than big cities). Finland is the world happiest country (9th time in a row)

Postdoctoral research position at Princeton on psychological processes related to time (such as impatience and present bias) and their causes and political consequences.

Markus Prior writes:

I have an opportunity to hire a postdoc to work on an interdisciplinary project about time preference and patience in politics. The three most recent postdocs on the project have all gone on to assistant professor positions and are all still involved in the research. I am particularly interested in candidates with experience in experimental political science, behavioral economics, or political/social psychology (esp memory). The description of the position is copied below and listed here.

OK, by “behavioral economics,” I think he means “cognitive psychology,” but I get the idea.

From the project description:

The School of Public and International Affairs at Princeton University, invites applications for a Postdoctoral Research Associate or more senior research position. Applications are welcome from recent Ph.D.s and Ph.D. students who expect to earn their Ph.D. by the start date. The position will start on or about September 1, 2026.

The researcher will be asked to participate in the Time in Politics Project that focuses on psychological processes related to time (such as impatience and present bias) and their causes and political consequences. The individual will work on aspects of the project that may include analysis of existing survey datasets, development of original surveys, and experimental designs. The project will combine a variety of different research approaches. . . .

The successful candidate will have research experience in political psychology, behavioral economics, or cognitive/social psychology, and a strong background in quantitative research and statistical methods. Experience with questionnaire development and survey design software (e.g., Qualtrics, oTree) is highly desirable, as are data management and programming skills, preferably in STATA. (STATA will be used for this project, but proficiency in equivalent programming languages is acceptable as long as the candidate is willing to learn STATA enough to manage and extend the project files.) The candidate should be well organized, attend to detail, and respond to deadlines in a timely fashion.

OK, that last bit seems appropriate. If you’re gonna work on project about time preference and patience, you should be able to respond to deadlines in a timely fashion. I’ve actually never seen such a condition in a job description. Isn’t that assumed that if you take a job that you’ll do things on time?

Also kinda funny that they’re using Stata. Maybe the postdoc will be able to talk the PI’s into switching to Python or whatever.

In all seriousness, Prior has done and continues to do important work on political information on public opinion, so this sounds like a great project for someone with the right background and interests.

I wonder if the project will get into the debates regarding the effects of the economy on voting: to what extent are voters reacting to recent economic conditions, or reports of economic conditions, or something else?

Survey Statistics: Blue Rose Research is hiring again !

As readers may know, I’m a survey statistician at Blue Rose Research. We survey the public to forecast elections and test political messages, used to advise Democrats. On this blog we’ve discussed our 2024 election retrospective and announced hiring (April 2025 and October 2025). And now we hiring again !

We are hiring a Data Scientist – Machine Learning (salary: $140k – $190k). This will be with our forecasting team, who track public opinion, forecast election results, develop resource allocation tools, and deliver polling analysis that clients rely on to set strategy. We’ve built a technical stack that enables cutting-edge statistical, machine learning, and engineering solutions.

Any additional roles we open up will be updated here. There’s an option to sign up for alerts for new roles via email if you’d like.

All positions are remote, with an option to be in person in New York City.

Please circulate and apply !

Postdoc in Milan on scalability for high-dimensional Bayesian learning

Giacomo Zanella writes:

I am currently advertising a postdoc position based at Bocconi University in Milan, supervised by myself and funded by the ERC Starting Grant, “Provable scalability for high-dimensional Bayesian learning.” The duration of the position is until 30 April 2028. Short extensions might be possible depending on availability of further funding.

The deadline for application is 03 March 2026 and the planned starting date is 1 July 2026 or later. Candidates are welcome to write to me at [email protected] with any question about the position.

Here’s the webpage of their Bayesian learning laboratory. Looks cool!

I heard about this position from Bob Carpenter, who adds:

Giacomo’s done some of my favorite work in the field recently. His use of Picard iterations is where I first saw the MCMC as optimization thing last summer. He’s also the one who’s been publishing with Gareth about situations where Gibbs is faster than HMC, so you should be simpatico.

That sounds just great. They’re doing interesting work on an important set of topics.

Postdoctoral position at the University of Pennsylvania on the study of political information

Dan Hopkins writes:

The School of Arts and Sciences at the University of Pennsylvania, in partnership with the Penn Center on Media, Technology, and Democracy and the Annenberg School for Communication, invites applications for a postdoctoral researcher with experience in the quantitative and/or computational study of the political information system, especially news, media, advertising, political campaigns, and citizens’ reactions to them and voting behavior. The Knight Postdoctoral Fellow is intended for a quantitative researcher or computational social scientist with a background in political science, communication, or computer science, or another relevant discipline interested in studying topics including news content production, diffusion, and consumption, political agenda-setting, and the impact of emerging technologies and communication media on citizens’ political attitudes and behaviors.

The Knight Postdoctoral Fellow may have access to large and highly granular datasets of media consumption and content through the Computational Social Science Lab as well as extensive survey and election data. Therefore, ideal candidates will have expertise in computational social science (especially machine learning and natural language processing) and experience working with large media and/or textual datasets. Interest in advancing methods of computational social science, such as experiments using conjoint designs or LLM-generated treatments, is preferred. We anticipate a 50/50 split between the postdoc working on projects identified by the supervisors and on projects of their own choice.

This postdoc will join a cohort of Knight PhD and Postdoctoral Fellows affiliated with the Penn Center on Media, Technology, and Democracy, attending a regular Information & Democracy Research Seminar and participating in other Center events and programming.

The Knight Postdoctoral Fellow will be co-supervised by Julie and Martin Franklin Presidential Professor Daniel J. Hopkins and Annenberg School of Communication Associate Professor Yphtach Lelkes. The position is expected to start in Summer or early Fall 2026. The initial appointment is one year with a possible extension for a second year based on satisfactory performance, ongoing funding and supervisor availability, and mutual agreement.

Qualifications:

Candidates should show outstanding capacity for research, as well as excellent communication skills apparent through their writing sample, references, and possible interviews. Applicants must have a Ph.D. by the time of appointment (which is expected to be around 9/1/2026) from an accredited university in a relevant field, including Political Science, Computational Social Science, Communication, Computer Science, Information or Network Sciences.

Application Instructions

To apply, please submit the following materials via email to: [email protected]

Please mention the “SAS Knight Postdoctoral Fellow” in the subject line.

Applications will be considered on a rolling basis until the position is filled. Interested candidates are encouraged to submit the following materials by February 13, 2026:

  • Cover Letter
  • Curriculum Vitae
  • Research Statement
  • Selected Publications (no more than 2 articles/chapters totaling 100 pages)
  • Contact Information for three references

The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.

URL: https://infodem.upenn.edu/sas-postdoc/

Political communication is important, I guess now more than ever with the continuing displacement of trusted objective news media by partisan outlets. And this is a good research team.

How the new era of CEO supervillains are trapped in their own ideology

I’m using the word “ideology” here not in the sense of political ideology but rather their view of tech innovation, in which successful innovations lead to big companies which become dinosaurs that get defeated by the next generation of plucky upstart mammals. Rich and powerful tech investors and executives see themselves as being the previous generation of upstarts and they’re painfully aware of the possibility that they’ve become the dinos. Paradoxically, the story they tell about their specialness as founders is embedded in a framework that implies future creative destruction at their expense, leading to an insecurity that drives them to do bad things.

It’s interesting because a standard role of economic ideology is to justify the positions of the wealthy. In this case, yes, the economic ideology justifies their existing position but it also implies future uncertainty. And, in an appointment in Samarra sort of way, every step they take to avoid the future reckoning just makes this uncertainty worse. Leading to tragedy for these tech executives and also for the rest of us.

I thought about all this after reading Careless People, a memoir by Sarah Wynn-Williams, a lawyer from New Zealand, about her several years as a Facebook executive. It follows her trajectory from idealism through enthusiasm, excitement, intensity, disillusionment, resistance, and departure. One thing I appreciated is that Wynn-Williams doesn’t present herself as a victim. In the book she’s a competent and resourceful person who eventually finds herself in over her head. And she’s got great stories. I don’t know what’s true, what’s exaggerated, and what’s left out–I don’t know any of the people involved, but it all sounds plausible. Her superiors in the organization seem like a bunch of liars, but I guess that some of that is helpful for attaining success in this ever changing world in which we live in. There were some scary bits like when they pressure her to take a long-distance flight when she’s in an advanced stage of pregnancy, and a funny bit where Mark Zuckerberg is playing Settlers of Catan with the other senior executives, they all go easy on him to let him win, and Zuckerberg doesn’t realize this is happening. I guess that he outsources his people-reading skills.

One thing that struck me is that, somewhere in the middle of the book, Facebook moves from a traditional big company that tries to use some mixture of competition-busting tactics and lobbying to maintain its position as market leader, to a powerful entity in itself that negotiates to keep governments in power.

It’s kind of like, first they’re playing the business game according to its de facto rules, then they’re playing the version of the game of monopoly where if you’re powerful enough, you can try to rewrite the rules. They’re playing the game of meta-monopoly.

And that seems dangerous. It puts the executives in the “fiduciary” position in which they’re expected not just to play hard and not just to push the boundaries of the rules–the usual calculation is that breaking the law is ok as long as the expected benefits exceed expected costs, and when benefits are in the billions and fines are sporadic and in the millions, you can see where this is going–but also to change the rules. This is too much power, and it also seems corrupting. I feel like even the Facebook executives themselves–even the creepy ones who may have enjoyed being able to change the rules–were not served by this.

To use a saloon poker analogy for a moment: you can think of Facebook as a successful poker player with a huge bankroll, playing a largely on-the-level poker game, with some collusion, some stacked decks, etc., but still mostly poker–but now someone comes in and hands Facebook a revolver. Cool! Now then can really make bank. But the poker game won’t last so long anymore. Nobody wants to play a game where they don’t have a chance. They move from lobbying the government, to being partners with the government and getting special advantages, to needing the government to keep the game going, maybe even infusing the game with tax dollars in some way. I’m not saying this has all happened yet, just that it’s the trajectory. I think they’d be better off if they were still playing in a straight-up poker game, but I can see that once they had the opportunity to grab more, it was hard for them to say no.

The ideology’s in trouble

The trajectory of Facebook gives me some insight into the inherent incoherence of the ideology of market-leading tech companies. I have the impression that their ideology has three components:

1. The company was founded from a combination of inspiration, brilliance, hard work, ruthlessness, and luck. The right people at the right time putting in all nighters and refusing to take no for an answer. The startup is the creative mammal thriving beneath the notice of the lumbering dinosaurs.

2. As the company gets big, it needs to avoid becoming one of those dinosaurs. So it should never lose its startup habits: openness to wild new ideas, thinking big, willingness to work long hours, and commitment to the cause. The challenge is to stay young and hungry even while the company is becoming middle-aged and fat.

3. The goal is 20% annual growth forever, or until the heat death of the universe, whichever comes first.

OK, you can see some contradictions here! On one hand, the storyline is that you’re gonna get overtaken by hungry young newcomers; on the other hand, you’re supposed to stay on top forever. The result, at least for Facebook, seems to have been a kind of desperation, a sense that on one hand they are the kings of the world and that on the other hand they are destined to fail and so they have to try harder and harder to grow and grow and preserve a near-monopoly status. And that’s how you get these executives who control unimaginable fortunes and yet are willing to lie and cheat (I wanted to say “lie, cheat, and steal,” but I don’t know if there was any actual stealing reported in that book), indeed they seem to feel that they have to like and cheat and manipulate the rules and all of this to stay on top.

This is where I feel like their ideology is killing them. Yeah, it’s good that they recognize that as businesspeople they’re nothing special–they just happened to be in the right place at the right time–and it’s good that they recognize that a company has a natural life cycle and you can’t stay on top forever. The bad thing here is that it gives them such a sense of existential insecurity that they feel that they have to keep reinventing themselves and their businesses. They seem to feel a kind of duty to keep the growth going, even while they recognize that there’s no reason they shouldn’t be supplanted by the new generation, and that motivates them to do bad things.

Again, I think they’d be better off if they weren’t able to change the rules in this way–they’d be better off if they were just selling widgets and following the usual corporate playbook. This growth-or-die attitude is just ruining these people.

Post-doc positions in Finland

This post is by Aki.

ELLIS Institute Finland is a newly established world-class research hub in AI and machine learning – and we are growing! The positions are within in one of the member universities (including Aalto University where I am).

We are now seeking postdoctoral researchers to join ELLIS Institute Finland and its wider research community. We welcome applications across all areas of machine learning, artificial intelligence, and related fields like statistics. Your research can be theoretical, applied, or span both. There are few PIs including me working also on Bayesian inference, modeling and workflow, and thus this call is great also for someone who likes these topics.

See more at the postdoc call web page.

The Mets are hiring

Sam Saskin writes:

I’m reaching out because we are hiring for a couple of jobs on the Mets analytics team and I was wondering if you’d be willing to share the job postings on your blog. The two positions (posting links below) are Senior Data Scientist, which would be a match for anyone looking for a full-time position, and Data Science Intern, which would be a match for current students (either undergraduate or graduate) who would be interested in spending a summer working with our team. I really appreciate the assistance, as we’ve had a lot of luck finding great candidates through visibility on your blog in the past.

Also if you have a 100 mph fastball they might be able to find a place for you somewhere in the organization.

“The Great Believers” by Rebecca Makkai

The author Rebecca Makkai has come up a couple times already in this space, first in a quick mention regarding the character names in her excellent recent novel, I Have Some Questions for You, then later in a discussion of the structure of suspense in literature. Also she has a blog that’s included in the Cultural section of our links page.

This all came up because I just finished another Makkai novel, The Great Believers, which I also liked a lot. I’d say I “enjoyed” it–I think that’s an accurate description of a book that I read a few pages every day at bedtime until one day I just sat on the couch and read the last 30 pages–except that the story is sad, so in that sense the reading experience wasn’t quite enjoyable. In any case, I recommend it.

The book did something with time that really worked. Chapters alternated between 1985 and 2015, where you see some of the characters from the earlier time period reappear. This gives some perspective (as logician and sometime Chicagoan Raymond Smullyan memorably said, “To know the past, one must first know the future.”). But it’s not just that. There’s some way in which placing the main action in the 1985, but situating the story in the present time, makes the people in 1985 seem more alive then the would feel in a straight-up historical novel. I’m not quite sure why this would be, and I don’t think the parallel-time-line thing always works–indeed, I’ve read novels that use it and it seems more like a gimmick, underlying the artificiality of the story–but in this case I found it to be very moving.

One more thing. From the author description:

Makkai is on the MFA faculties of the University of Nevada, Reno at Lake Tahoe and Northwestern University, and is the artistic director of StoryStudio Chicago. She lives on the campus of the midwestern boarding school where her husband teaches, and in Vermont.

Which leaves me with some questions:

1. If it’s “the University of Nevada, Reno at Lake Tahoe,” why don’t they just call it “the University of Nevada, Lake Tahoe”?

2. I wonder if she knows the story of the torment executioners at the University of Nevada, Reno? I have a feeling that the characters in The Great Believers would be very amused by the idea of a torment executioner.

3. Makkai has 3 jobs in 2 different states, and then also lives in a third state? How does she do it? I’m reminded of this guy:

Niall Ferguson, MA, D.Phil., is the Laurence A. Tisch Professor of History at Harvard University. He is a resident faculty member of the Minda de Gunzburg Center for European Studies. He is also a Senior Research Fellow of Jesus College, Oxford University, and a Senior Fellow of the Hoover Institution, Stanford University.

I guess some people just have a lot of energy. I’m pretty sure that Ferguson would hate Makkai’s book, though, given that he’s famous for a homophobic slur and The Great Believers is a sympathetic portrayal of gay life.

Postdoc opportunity at Stanford and Chicago on Bayesian hierarchical modeling and partial pooling for improving the accuracy and equity of property tax assessments

Evelyn Smith writes:

I’m reaching out because Stanford RegLab and the Mansueto Institute at the University of Chicago are beginning a search for a postdoctoral scholar, and I wanted to ask whether you might recommend any strong PhD students or recent graduates.

We’re seeking candidates with expertise in Bayesian hierarchical modeling, partial pooling, geostatistics, or related areas for applied work using fine-grained geographic data to improve the accuracy and equity of property tax assessments. The role would be a joint position between Stanford and U Chicago.

Because we hope to hire by early spring (March/April), any referrals you may have would be greatly appreciated. Please feel free to share this note with anyone who might know promising candidates. I’m happy to speak directly ([email protected]) with anyone who’d like to learn more about the project or role.

This looks interesting, also it’s an important topic. And I’ll add this: if you’re doing statistics or social science, it’s super important to work on real problems–not just “real data” but what I call “live problems” where stakeholders really care about the answers, not just to get publications or whatever but because there are policy implications. This seems to be the case here, also of course it’s a good sign that they’re already interested in Bayesian hierarchical modeling and partial pooling.

From one perspective, “property tax assessments” could sound kinda boring. But it’s a big deal in this country. So go at it!

Effective sample size

This post is by Aki

Richard McElreath had a bsky post with a MCMC convergence diagnostic plot with one axis label saying “number of effective samples”. I commented that it’s wrong and misleading, and it would be better to write “effective sample size”. Frank Harrell asked elaboration. Before I had time to answer, some other people had posted just fine answers. I wanted to write a bit more but not having time to write short, I decided to write a blog post.

The problem with “number of effective samples” is that it sounds like some samples are effective and some are not, but the effectiveness is not a property of individual samples but the whole sample.

Before I continue further, I want to switch using different words. Each individual posterior draw is sample, and the collection of all posterior draws is sample. Technically this is correct, but can lead to confusion whether we refer to an individual (sample) or the group of individuals (sample). This is why I prefer to talk about individual posterior draws, and the collection of posterior draws is a posterior sample. This is also why posterior R package uses the term draw.

Every single MCMC draw has effective sample size 1, and the number of effective draws is the same as the total number of draws. However, when we use a collection of MCMC draws to estimate some expectation, the Markovian dependency makes the Monte Carlo error behave in a way that it makes sense to compare the estimation efficiency to the corresponding sample size of independent draws.

Effective sample size used to be denoted by n_eff. It might be that “number of effective samples” comes from some people reading n_eff as “number of effective”. n_eff has another problem, as n often denotes the number of observations. That’s why we recommend shortening the effective sample size as ESS. This is also what posterior package uses.

A further important point is that the effective sample size depends also on which expectation is estimated. Most commonly effective sample size has been reported for estimation of E[theta], but as the effective sample size can be very different for example for E[theta^2] it would be better to explicitly state what is estimated. By default, posterior package reports Bulk-ESS and Tail-ESS, and while neither is ESS for a simple expectation, they are more informative than just ESS.

It’s been a long time since I wrote a blog post which was not a job ad, and coincidentally also now I’m looking for postdocs who have strong background in Bayesian methods and interested to work on Bayesian cross-validation, model checking and comparison.

MSc and PhD programs in statistics at the University of British Columbia

(this post is by Charles)

Applications for the MSc and PhD programs in statistics at the University of British Columbia are now open. The application deadline is December 1st for PhDs and January 5th for MSc.

Of interest to the readership of this blog is that we have a lot of momentum in the department for research in Bayesian modeling, computational statistics, and probabilistic machine learning. Amongst the new faculty hires, Saif Syed and myself are both starting research groups to work on algorithms for probabilistic modeling. And we have an established guard working on Bayesian computation, that includes Alexandre Bouchard-Côté, Trevor Campbell, and Geoff Pleiss.

Furthermore, the department stands out through its commitment to implementing novel methods in high-performance probabilistic software. In the department, you will find the creators and maintainers of Pigeon.jl and GPyTorch. And, as some of you may know, I’m myself a seasoned Stan developer. I’m also happy to report that we have some veteran Stan users on campus, notably in the Ecology department :)

We’re a quite large and growing department with interests that span all areas of statistics (not just Bayesian methods!). PhD students entering the program do not need to be matched up with an advisor upon admission—rather, you have one year to interact with faculty and explore a number of topics. The program does a really good job allowing and helping students do that!

Vancouver is a really beautiful and bustling city. The campus is right by the ocean and you’re a one hour drive away from hiking in the mountains. I’m new and still figuring out the place, but so far, I’ve found the quality of life here to be very good. By the way, it doesn’t get that cold, because of the ocean currents and we rarely get snow in the city (but we still have skiing nearby). We do get rain and somewhat British weather…

One final point: Unlike in the U.S., application to the PhD program requires a masters degree. However, if you only have a bachelor’s degree and are committed to pursuing a PhD, exceptional candidates can apply to the PhD track program within the MSc program.

The Office of Risk Assessment at the Netherlands Food and Consumer Product Authority is looking for an applied statistician with expertise in Bayesian statistics or causal inference

Joost Meekes writes:

At the Netherlands Food and Consumer Product Authority (NVWA), Office of Risk Assessment, we have a vacancy for an applied statistician (or a data scientist with expertise in statistics). We are particularly interested in candidates with knowledge of and experience with Bayesian statistics or causal inference. If you know anyone who might be interested in this position, or if you would publish the vacancy on your blog, we would be most grateful.

The position requires proficiency in Dutch.

The Netherlands Food and Consumer Product Authority is a government agency which oversees a wide variety of domains, working to guarantee public interests including food and product safety, plant health, and animal health and welfare. The position offers the opportunity to work on a wide variety of applied statistical and machine learning problems with societal impact and comes with excellent benefits.

This sounds really cool, also a great opportunity to improve jouw nederlands.

Survey Statistics: Blue Rose Research is hiring !

As readers may know, I’m a survey statistician at Blue Rose Research. We survey the public to forecast elections and test political messages, used to advise Democrats. We’ve announced hiring here a few times (e.g. in April 2025). We’ve discussed our 2024 election retrospective. And now we hiring again ! Looking for experts at the intersection of AI and statistical modeling.

We estimate causal effects of in-survey political messages using scaled-up versions of MRP. To get more insight, we connect this pipeline to new AI tools for generation and summarization. We are looking for a teammate with deep expertise in both LLM tools and statistical modeling to build tooling that scales our analyses with care and thoughtfulness. We want a teammate who clearly communicates assumptions, results, and uncertainty. We are a mission-driven team that values kindness and collaboration.

For more details, see the job posting.

  • Salary: $140,000 – $190,000 annually, commensurate with experience.
  • Benefits: Competitive health, dental, and vision coverage; generous leave; and a supportive, mission-driven culture.
  • Work setup: Fully remote team with an NYC office and regular in-person meetups (NYC & DC). Most of our work happens on East Coast time.

Please circulate and apply !

Assistant professor position at USI in Lugano

This post is from Bob

Antonietta Mira writes,

Assistant Professor position (tenure-track) in Theoretical Data Science and Machine Learning, Università della Svizzera italiana, Lugano, Switzerland

USI is looking for a talented junior statistician for Tenure Track Assistant Professor position in “Theoretical Data Science and Machine Learning”. The deadline for applications is 30 November 2025 and we are looking for a starting date around the Summer 2026. Starting salary is 110,000 CHF per year. More details about the required profile can be found on:

Official announcement.

The faculty is located in Lugano in the South of Switzerland on the University’s main campus, 1.5 hrs from Zürich and 1 hr from Milan. It has been expanding its activities in Data Science, Statistics and Machine Learning. Since 2024, we are now having both a bachelor’s and master’s degree in Data Science and a master’s degree in Artificial Intelligence. In August 2026, we will be hosting the European Meeting of Statisticians in Lugano.
Timeline of the hiring process

  • 30 November 2025: application deadline
  • 2-4 February 2026: In person interviews in Lugano, Switzerland.
  • Summer 2026: envisioned starting date

For any questions, do not hesitate to contact the Dean, Prof. Ernst C. Wit ([email protected]).

Mitzi and I have visited Antonietta for a week on two separate occasions over the last few years and we absolutely loved Lugano the city, the USI campus, and getting to hang out with Antonietta. Teaching is in English and faculty salaries in Switzerland are much better than anywhere else I know in Europe (1 Swiss franc is about US$1.25).