position in statistics and/or machine learning at ENSAE ParisTech‐CREST

ENSAE ParisTech and CREST are currently inviting applications for a position of Assistant or Associate Professor in Statistics or Machine Learning.

The appointment starts in September, 2019, at the earliest. At the level of Assistant Professor, the position is for an initial three-year term renewable for another three years before the tenure evaluation. Salary is competitive according to qualifications. The teaching duties are reduced compared to French university standards. At the time of appointment, knowledge of French is not required but it is expected that the appointee will acquire a workable knowledge of French within a reasonable time.

Candidate Profile

– PhD in Statistics or Machine Learning.
– Outstanding research, including subjects in high-dimensional statistics and machine learning.
– Publications in leading international journals in Statistics or leading outlets in Machine Learning.

Demonstrated ability to teach courses in Mathematics, Statistics and Machine Learning for engineers and to supervise projects in Applied Statistics. The successful candidate is expected to teach at least one course in mathematics, applied mathematics or introductory statistics at the undergraduate level, and one course in the “Data Science, Statistics and Machine Learning”’ specialization track during the third year of ENSAE (Master level).

Applications should submitted (in French or in English) by email to recruitment@ensae.fr :
– Curriculum vitae;
– Statement of research and teaching interests (2-4 pages);
– Names and addresses of three or more individuals willing to provide letters of reference.

Deadline for applications : April 29, 2019.
Selected candidates will be invited to present their work and project at ENSAE‐CREST.

position at Harvard

This to point out an opening for a tenure track position in statistics and probability at Harvard University, with deadline December 1. More specifically, for a candidate in any field of statistics and probability as well as in any interdisciplinary areas where innovative and principled use of statistics and/or probability is of vital importance

data scientist position

Our newly created Chaire “Economie et gestion des nouvelles données” in Paris-Dauphine, ENS Ulm, École Polytechnique and ENSAE is recruiting a data scientist starting as early as May 1, the call remaining open till the position is filled. The location is in one of the above labs in Paris, the duration for at least one year, salary is varying, based on the applicant’s profile, and the contacts are Stephane Gaiffas (stephane.gaiffas AT cmap DOT polytechnique.fr), Robin Ryder (ryder AT ceremade DOT dauphine.fr). and Gabriel Peyré (peyre AT ceremade DOT dauphine.fr). Here are more details:

Job description

The chaire “Economie et gestion des nouvelles données” is recruiting a talented young engineer specialized in large scale computing and data processing. The targeted applications include machine learning, imaging sciences and finance. This is a unique opportunity to join a newly created research group between the best Parisian labs in applied mathematics and computer science (ParisDauphine, ENS Ulm, Ecole Polytechnique and ENSAE) working hand in hand with major industrial companies (Havas, BNP Paribas, Warner Bros.). The proposed position consists in helping researchers of the group to develop and implement large scale data processing methods, and applying these methods on real life problems in collaboration with the industrial partners.

A non exhaustive list of methods that are currently investigated by researchers of the group, and that will play a key role in the computational framework developed by the recruited engineer, includes :
● Large scale non smooth optimization methods (proximal schemes, interior points, optimization on manifolds).
● Machine learning problems (kernelized methods, Lasso, collaborative filtering, deep learning, learning for graphs, learning for timedependent systems), with a particular focus on large scale problems and stochastic methods.
● Imaging problems (compressed sensing, superresolution).
● Approximate Bayesian Computation (ABC) methods.
● Particle and Sequential Monte Carlo methods

Candidate profile

The candidate should have a very good background in computer science with various programming environments (e.g. Matlab, Python, C++) and knowledge of high performance computing methods (e.g. GPU, parallelization, cloud computing). He/she should adhere to the open source philosophy and possibly be able to interact with the relevant communities (e.g. scikitlearn initiative). Typical curriculum includes engineering school or Master studies in computer science / applied maths / physics, and possibly a PhD (not required).

Working environment

The recruited engineer will work within one of the labs of the chaire. He/she will benefit from a very stimulating working environment and all required computing resources. He/she will work in close interaction with the 4 research labs of the chaire, and will also have regular meetings with the industrial partners. More information about the chaire can be found online at http://www.di.ens.fr/~aspremon/chaire/

tenured research position with ABC skills!

I just received this announcement for the opening of a (tenured/civil servant) position in the national research institute in biostatistics, genetics, and agronomy, INRA:

Position opening with profile Approximate inference techniques in complex systems

Key activities and required skills: You will develop methodological research in the field of statistical inference for models used in environmental sciences. These inference techniques will account for the complex dependency structure due to the temporal, spatial and evolutionary organisation of the observations, for the heterogeneity of the data and for the existence of unobserved variables or incomplete data. Solid experience in statistical modelling of complex data (graphic models, multi-scale spatio-temporal data) and a strong orientation towards the applications in environment and biology would be appreciated. Skills in approximation techniques (variational inference, ABC techniques) will be welcome.

Contact person: Stéphane Robin (robin [chez] agroparistech [lepoint] fr)

Location:  Versailles-Grignon (Paris)

Deadline: February 25, 2012

Website: INRA offer

This should appeal to (some) readers of the blog, esp. since the offer has no nationality constraint.

Bayesian job in Cambridge

Here is an email that could appeal to some readers:

Job in Cambridge MRC-BSU – Bayesian statistician
Career development fellow
MRC Biostatistics Unit, Cambridge

We are offering an exciting opportunity to work on Bayesian models for infectious disease dynamics. A statistician is required to contribute to a programme of research to develop inferential approaches to estimation and prediction of epidemic evolution when relevant information, possibly from diverse sources, arrives at regular intervals, and, likely, in real time. Examples include the monitoring and prediction of long standing epidemics such as HIV, as well as new emerging epidemics (e.g. Swine Flu). Challenges include the need to synthesise heterogeneous and biased information to produce timely updates of epidemic evolution. You will have a PhD in statistics, or a relevant discipline; computing experience, both of statistical packages (e.g. R) and programming languages (e.g. C, C++); and experience of Bayesian statistics. Experience of Bayesian computation (e.g. MCMC) and of Bayesian inference for disease transmission would be advantageous. You must have good communication skills and be able to contribute substantially to writing scientific papers. Starting salary will be in the range of £26,022 – £28,764 per annum.

Applications are handled by the RCUK Shared Services Centre; to apply please visit our job board to view the full job description and person requirements and complete an online application quoting reference IRC18709. If you are unable to apply online please contact us on 01793 867003.

Closing date: 11th May 2011 Interview date: 25th May 2011