3 February 2018

The salary of CNRS researchers

UPDATE 03/02/2018:
  • The class and level structure has changed in 2017, so the discussion below is no longer up to date. However, the concrete difference in terms of remuneration should not be that large.
  • I updated the value of the index point.
  • I added at the end a graph summarizing the salary progression.
In a previous post I discussed the application process for tenured research positions with the CNRS. I did not go into the details on the remuneration, but I think this information could be useful. All CNRS personnel (researchers and technical staff) are state employees. The remuneration level is publicly available, but the information is mainly in French (see e.g. here), and not very legibly presented. Once again, this is my personal understanding and not the official position of the CNRS, I am not an accountant, use at your own risk etc.

First, a vocabulary point: The hierarchy is defined by three variables. They are given below starting with the most significant; their values are listed in order of increasing seniority.
  1. The rank (corps) : CR (chargé de recherche) or DR (directeur de recherche)
  2. The class (classe) : 2, 1 and (for DRs only) CE (classe exceptionnelle)
  3. The level (échelon): from 1 to 6 for CR2 and from 1 to 9 for CR1


Since the recruitment takes place almost exclusively at the CR (chargé de recherche) rank, this is what I will discuss in the following. The remuneration is expressed in index points. At the moment, one point is worth 56.2323 €: its value has increased very slowly over the last 12 years and does not compensate the inflation rate (see the graph below). The number of points is given in this table, to be used as follows:
  1. Each level (column 1) is reached after a seniority in that particular rank indicated in the fourth column (For CR2, use the number of years since the beginning of your PhD). The seniority is cumulative: to reach the 4th CR2 level you need 1+1+1 years of experience. The number of points corresponding to the level is given in the third column (indice majoré).
  2. Multiply this number by the value of the index point and divide by 12 to obtain the "gross" monthly salary (salaire brut mensuel).1 I use the inverted commas because this amount is after some contributions and taxes. In particular, this is not the total cost of employment!
  3. Multiply by about 0.83 to obtain the net salary (salaire net). This is the amount that you will effectively receive in your bank account every month. At this point, mandatory health insurance, retirement and all other contributions have already been subtracted, but you will of course need to pay income tax.
In the most common case (evoked by Julien Tailleur in a comment to the previous post), you will be recruited as CR2 with 5-7 years of experience (since starting your PhD). You'll get one year "extra" for the doctorate, so you'll probably be at the 6th CR2 level for four years, at an index of 564, corresponding to 2600 € gross and around 2200 € net.
    Note that the administration can take a few months to validate your work experience, time during which you will be paid a first-level salary. However, once the paperwork is done, you will retroactively receive the difference starting from your first day of employment.
      After four years you'll be promoted to CR1, directly to the 4th level, at an index of 623 (the top CR2 level corresponds to the 3rd CR1 level, but some of the seniority "carries over" from one class to the next).
        For your information, the remuneration level for all CNRS positions is also available.


        Aside from the basis salary calculated above, you can also receive:
        1. A statutory bonus: Automatically attributed to all researchers, it amounts to 340 € (CR2) or 450 € (CR1) twice a year.
        2. A performance bonus: Reserved to the best and brightest (as defined by the CoNRS committees).

        Extra income

        Researchers are allowed to supplement their salary within certain limits. The most common supplementary activities are teaching and consulting.


        As a concrete example, I show below the evolution of my net annual salary (in nominal euros and inflation-corrected), including the statutory bonus. It amounts to an annual raise of 1.4% (on top of the inflation). The 2006 value is above the 2007 one because I had received an amount that was due for 2005.
        Clearly, this increase is mostly due to the seniority progression (in level and class), as the base rate (index point) progresses embarassingly slowly (much more slowly than the inflation, for instance).

        1. The gross salary can also include a small residence bonus and a family contribution (depending on the number of children), but I neglected them in the calculation. See here for more details.

        20 January 2018

        What rank for Paris-Saclay?

        The Paris-Saclay University was officially created on December 29, 2014, but in some shape or another the project had started as early as 2010 or even in 2008, depending on the point of view. Among the declared goals of this endeavour was improving (by the year 2020) the position of the new university in the Shanghai (ARWU) ranking with respect to the Paris-Sud University, which is the main partner to the project.
        It is then interesting to compare the current ranking of Paris-Sud University (blue dots in the Figure above) and the predicted ranking of Paris-Saclay (letters and red error bars).

        A) The first prediction was made in 2012 by Dominique Vernay, chair of the Foundation for Scientific Cooperation (FSC) Paris-Saclay Campus (top official of the project at the time), who was confident that the new institution would reach the top 10 "of the most attractive universities in the world" [1,2]. This declaration was generally understood at the time as referring to the ARWU ranking, where the size increase of the institution would have the largest effect.

        B) The 2017 objectives of Paris-Saclay are more modest, only aiming for the top 20 of the ARWU ranking. This estimation is supported by 2015 projections that predict a rank between 18 and 26 [1,2].

        11 December 2017

        CNRS positions - the 2018 campaign

        The detail of the 2018 campaign for permanent research positions at the CNRS (Centre national de la recherche scientifique) has been published in the Journal Officiel (see links below) and the submission site is open. The submission deadline is January 8th 2018. There are 293 open positions at the CR level 211 (7 more than last year) 256 DR2 (-4) and 2 DR1 (same as last year). The total number has been stable over the last few years, as shown in the graph below:
        An important change has been the merging of the two former CR ranks (second and first class) into a single one "normal class", or CRCN and the creation of an "exceptional class" (by promotion only, I suppose).

        This simplification makes a lot of sense, since historically (i.e. 25 years ago) CR2 positions were filled with very junior scientists, typically fresh out of their PhD. Nowadays, they are much more experienced (at least three years of postdoc) so the distinction was less and less relevant, especially since the removal of the age limit. 

        However, a difference in age and the level of experience between the CR2 and CR1 laureates still persisted and it will be interesting to see how the recruitment committees will react to the merger: push the CRCN level to that of the CR1 competititon or maintain an informal separation and keep on recruiting some younger candidates?

        The official texts: CRCN, DR2, DR1.

        5 November 2017

        Reversible strain alignment and reshuffling of nanoplatelet stacks confined in a lamellar block copolymer matrix

        Our paper has just been published in Nanoscale!

        We show that the orientation and stacking state of nanoplatelets confined within a polymer matrix can be reversibly controlled simply by pulling on the material.

        20 September 2017

        Normic support and the revision of prior knowledge

        In three previous posts I discussed Martin Smith's paper "Why throwing 92 heads in a row is not surprising" [1]. I attempted a Bayesian interpretation of the concept of surprise, but I was sure that this had already been done before; a cursory literature search confirmed this impression (see below). Can one go further? Martin relates surprise to the more general concept of normic support, and the obvious question is whether the latter can also be interpreted in Bayesian terms.

        I'll use the example of judicial evidence, that Martin treats in Ref. [2], where normic support is defined as follows :
        a body of evidence E normically supports a proposition P just in case the circumstance in which E is true and P is false would be less normal, in the sense of requiring more explanation, than the circumstance in which E and P are both true.
        The Blue Bus paradox can then be solved by arguing that testimonial evidence has normic support, while statistical evidence does not ([2], page 19). To put it in the terms above, finding out that the testimonial evidence is false would surprise us, while the failing of statistical evidence would not.

        Can we restate this idea in terms of belief update, as I tried to do for surprise and, in particular, is the distinction between testimonial and statistical evidence similar to that between the coin throw and the lottery examples I drew here? The proposition P being in both cases "the bus involved was a Blue-Bus bus", we need to identify the evidence E of each type.
        1. testimonial: the witness can identify the color of the bus with 90% accuracy.
        2. statistical:   90% of the buses operating in the area on the day in question were Blue-Bus buses.
        By quick analogy with the respective coin throw and the lottery examples, respectively, we can then say:
        1. If the witness is wrong, the result
          • calls into question his/her priorly presumed accuracy and prompts us to revise our estimate (Bayesian interpretation.)
          • surprises us and requires more explanation (normic support perspective.)
        2. If the bus is not blue then, although non-blue buses only account for 10% of the total,
          • since the Blue-Bus deduction was merely based on the proportion of each type of bus there is no prior knowledge to revise (Bayesian interpretation.)
          • the result is unlikely but not abnormal, and thus it does not call for further explanation (normic support perspective.)
        I'll discuss in future posts how similar the two interpretations are and whether they solve the paradox (right now my feeling is that they don't, but I need to think about it some more.)

        Bayesian surprise

        A reference on the Bayesian treatment of surprise, defined as the Kullback-Leibler divergence of the posterior distribution with respect to the prior one.
        Itti, L., & Baldi, P. F. (2006). Bayesian surprise attracts human attention. In Advances in neural information processing systems (p. 547–554).

        1. Martin Smith, Why throwing 92 heads in a row is not surprising, Philosophers' Imprint (forthcoming) 2017.
        2. Martin Smith, When does evidence suffice for conviction?, Mind (forthcoming.)

        17 September 2017

        Some choices are not surprising

        In two previous posts I discussed Martin Smith's paper "Why throwing 92 heads in a row is not surprising" [1].

        I argued that the all-heads sequence is more surprising than a more balanced one (composed of roughly equal numbers of heads and tails), although the two have the same probability of occurrence based on the prior information (fair and independent coins), because the first event challenges this information, while the second does not.

        Prompted by an email exchange with Martin (whom I thank again for his patient and detailed replies!) I would like to discuss here cases where I believe no particular outcomes would be surprising. Let us take an example from the same paper, the lottery. I agree with the author that a draw consisting of consecutive numbers (e.g. 123456) is not surprising, nor is any other pattern or apparently random sequence, which all have the same probability of occurrence.

        In my view, this is simply because –for the lottery case– equiprobability assumption is just an uninformative prior. Finding a patterned sequence does not challenge our conviction, because there is nothing to challenge: any outcome will do equally well. For the coin toss, on the other hand, the equiprobability of all sequences stems from the very strong conviction that the coins are (1) unbiased and (2) independent, and the all-head outcome does challenge it.

        1. Martin Smith, Why throwing 92 heads in a row is not surprising, Philosophers' Imprint (forthcoming) 2017.

        12 September 2017

        Impressions from ECIS 2017 - day 2

        Highlights from the morning session of the second day. I managed to miss Jacob Klein's plenary talk (on Interfacial water).

        Parallel session on topics 5 and 6 (roughly, inorganic colloids)
        • Andrés Guerrero-Martínez (Madrid University) on the reshaping, fragmetation and welding of gold nanoparticles using femtosecond lasers.
        • Two more talks on responsive Au@polymer systems: Jonas Schubert (Dresden University) and Rafael Contreras-Cáceres (Málaga University)
        • Pavel Yazhgur (postdoc at the ESPCI, Paris after a remarkable PhD at the LPS, Orsay!) on hyperuniform binary mixtures. I should write a post on hyperuniformity at some point...
        Parallel session on topic 3 (polymers, liquid crystals and gels)
        • Hans Juergen Butt on the crystallization of polymers or water in alumina pores.

        4 September 2017


        I went jogging in the Buen Retiro park this evening. It reminds me of the Parc de la Tête d'Or, where I used to run many years ago. The difference is that back then I would overtake pretty much everybody. Nowadays, it's the other way round.

        Impressions from ECIS 2017

        I'm in Madrid for the 31st conference of the European Colloid and Interface Society. Here are some highlights from the morning session:

        Michael Cates on active colloids (plenary session)

        I arrived late and missed some of this talk, plus I'm not a specialist in the area of active colloids. What I found interesting is the search for the minimal modification of the various Hohenberg-Halperin models (B and H) that yield interesting behaviour; I still haven't understood how breaking the time-reversal symmetry comes into play. Here is a reference I promised myself I would read on the flight back home.

        Parallel session on topics 5 and 6 (roughly, inorganic colloids)

        Two talks on secondary structures in gold nanoparticle systems with potential applications to SERS:
        Two other talks focused on magnetic nanoparticles:
        • Laura Rossi (Utrecht University) on the self-assembly of hematite cubes (paper not yet published).
        • Golnaz Isapour (Fribourg University, in the group of Marco Lattuada) on color-changing materials based on responsive polymers (pNIPAM for temperature and PVP for pH).
        Aside from the nice work, the last talk also references a paper on Color change in chameleons, from which I learned that structures that generate structural colors are called iridophores (great name!), in contrast with the pigment-bearing chromatophores. I have already written about structural colors on this blog.

        18 August 2017

        Surprise and belief update

        In a previous post, I started discussing a paper [1] on the (un)surprising nature of a long streak of heads in a coin toss. My conclusion was that the surprise is not intrinsic to the particular sequence of throws, but rather residing in its relation with our prior information. I will detail this reasoning here, before returning to the paper itself.

        Let us accept as prior information the null hypothesis \(H_0\) "the coin is unbiased". The conditional probabilities of throwing heads or tails are then equal: \(P(H|H_0) = P(T|H_0)=1/2\). With the same prior, the probability of any sequence \(S_k\) of 92 throws is the same: \(P(S_k|H_0) = 2^{-92}\), where \(k\) ranges from \(1\) to \(2^{92}\).

        Assume now that the sequence we actually get consists of all heads: \(S_1 = \lbrace HH \ldots H\rbrace\) What is the (posterior) probability of getting heads on the 93rd throw? Let us consider two options:
        1.  We can hold steadfast to our initial estimate of lack of bias \(P(H|H_0) = 1/2\).
        2. We can update our "belief value" and say something like: "although my initial assessment was that the coin is unbiased [and the process of throwing is really random and I'm not hallucinating etc.], having thrown 92 heads in a row is good evidence to the contrary and on next throw I'll probably also get heads". Thus, \(P(H|H_0 S_1) > 1/2\) and in fact much closer to 1. How close exactly depends on the strength of our initial confidence in \(H_0\), but I will not do the calculation here (I sketched it in the previous post).
        I would say that most rational persons would choose option 2 and abandon \(H_0\); holding on to it (choice 1) would require an extremely strong confidence in our initial assessment.

        Note that for a sequence \(S_2\) consisting of 46 heads and 46 tails (in any order) the distinction above is moot, since \(P(H|H_0 S_2) =P(H|H_0) = 1/2\). The distinction between \(S_1\) and \(S_2\) is not their prior probability [2] but the way they challenge (and update) our belief.

        Back to Martin Smith's paper now: what makes him adopt the first choice? I think the most revealing phrase is the following:

        When faced with this result, of course it is sensible to check [...] whether the coins are double-headed or weighted or anything of that kind. Having observed a run of 92 heads in a row, one should regard it as very likely that the coins are double-headed or weighted. But, once these realistic possibilities have been ruled out, and we know they don’t obtain, any remaining urge to find some explanation (no matter how farfetched) becomes self-defeating.[italics in the text]

        As I understand it, he implicitly distinguishes between two kinds of propositions: observations (such as \(S_1\)) and checks (which are "of the nature of" \(H_0\), although they can occur after the fact) and bestows upon the second category a protected status: these types of conclusions, e.g. "the coin is unbiased" survive even in the face of overwhelming evidence to the contrary (at least when it results from observation.)

        There is however no basis for this distinction: checks are also empirical findings: by visual inspection, I conclude that the coin does indeed exhibit two different faces; by more elaborate experiments I deduce that the center of mass is indeed in the geometrical center of the coin, within experimental precision; by some unspecified method I conclude that the "throwing process" is indeed random; by pinching myself I decide that I am not dreaming etc. At this point, however, the common sense remark is: "if you want to check the coin against bias, the easiest way would be to throw it about 92 times and count the heads".

        If we estimate the probability of the observations (given our prior belief) we should also update our belief in light of the observations. Recognizing this symmetry gives quantitative meaning to the "surprise" element, which is higher for some sequences than for others.

        1. Martin Smith, Why throwing 92 heads in a row is not surprising, Philosophers' Imprint (forthcoming) 2017.
        2. We only considered here the probabilities before and after the 92 throws. One might also update one's belief after each individual throw, so that \(P(H)\) would increase gradually.