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Faculty position Physics data science Université Paris

Mar 12, 2021

The Université de Paris (https://u-paris.fr) opens a Professor position with a broad interdisciplinary profile centered on the use of the data science and its applications.

The considered range of applications is broad and ranges from physical or computer sciences to linguistics. The astroparticle physics and cosmology is among the themes considered. The applicant will have a primary affiliation to an institute of its own discipline (for Physics, this will be AstroParticule et Cosmologie APC http://www.apc.univ-paris7.fr), and will also be affiliated with the Paris Data Intelligence Institute (diiP) https://u-paris.fr/diip/.

He or she will present a research project in his or her field and will show a compelling interest in interdisciplinary work and the development of advanced data analysis methods. The profile sought is that of a confirmed researcher or lecturer.

The official publication of the position is in progress and will be carried out mid March. Interested persons can already take contact with Eric Chassande-Mottin, ecm(at)apc.in2p3.fr.

 

Job profile :

We are currently witnessing a tremendous increase in the amounts of data being produced and/or collected by various applications and scientific disciplines. This has been made possible with the recent advances in sensing instruments that allow the observation of different real- world phenomena at a scale and granularity that has never been possible, which in turn enables data-driven scientific discovery. In other words, we are now in a position to exploit the wealth of data to refine existing, or build new theoretical models, which describe and explain various phenomena in the real world. Examples range from life sciences (e.g., medicine, biology, neurolinguistics) and smart cities (such as manufacturing, transport), to Earth sciences (e.g., seismology, volcanology, oceanography), and physics and astrophysics (e.g., particle

physics, cosmology or gravitational-wave astronomy), where datasets can often grow to several TeraBytes (TB), or even PetaBytes (PB) in size.

Extracting knowledge from these data means that we need to perform analysis tasks that are becoming increasingly complex as the amount of data, the number of observed variables, and the levels of noise (e.g., when measuring weak signals) in the measurements grow. Therefore, we are in need of novel methods that can cope with the scale of data (TB to PB) and complexity of tasks (noise removal, detection of weak signals, classification of complex patterns, detection of anomalies/abnormal events) that we face across applications in different domains.

In order to address these challenges, we need to turn our attention to a set of new classes of Artificial Intelligence (AI), Machine Learning (ML) (both supervised and unsupervised; including deep learning), and scalable data analytics techniques, which produce very promising initial results.

The candidate will have a principal affiliation to a laboratoire of their main discipline, and will also be affiliated to the Data Intelligence Institute of Paris (diiP). The candidate will have recognized research work and convincing interest in interdisciplinary work, for developing novel methods in the intersection of AI/ML/statistical learning/data analytics/data intelligence that address fundamental challenges in modern science, industry, and society.

Below we list example profiles; other profiles that fit the perspective discussed above may also be relevant and will be examined.

Particle, or astroparticle physics and astronomy now regularly rely on advanced data analysis methodologies and machine learning techniques to analyze the experimental or observational data (e.g., object/signal detection and classification from large volumes of data in low signal-to-noise or confusion dominated regimes; or for the visualization of high- dimensional datasets). The development of those new approaches requires a candidate with a two-pronged expertise in both physics and computer science and/or statistical learning, with an interest to develop new mathematical tools that apply to the top questions in those fields, such as in the development of multi-messenger astronomy.

Linguistics traditionally makes the fundamental hypothesis that language has structure, in particular that sentences are recursively structured as trees. Some of the structural constraints have been claimed to underlie all human languages and not to be learnable. However, the recent emergence of deep learning and unsupervised methods, such as recurrent networks and transformers, provide new models, hypotheses and research directions that can be confronted with such approaches and may allow a deeper understanding of natural languages. The selected candidate will have a proven research program designing, analyzing and interpreting such models based on large corpora and/or experimental data (Eye Tracking, EEG, NIRS, fMRI) combining theory and experimentation and taking the diversity of languages into account. He or she is expected to have significant expertise in current research directions both in linguistics and in data science.

Computer Science is in a unique position to cater to the needs of modern data analytics applications, by combining a set of new classes of Artificial Intelligence (AI), Machine Learning (ML) (both supervised and unsupervised, with an emphasis on deep learning), and data analytics techniques that enable efficient and effective data processing at scale. The selected candidate will develop novel foundational methods at the intersection of AI/ML and large data management. The objective is to then use these techniques to address the needs of real-world applications by performing complex analytics tasks, and possibly by addressing additional challenges, such as data complexity, scale, dimensionality, heterogeneity, or noise.

*Research fields Euraxess

Astrophysics and particle physics Linguistics
Computer science

Enseignement (en précisant les mentions et/ou parcours, niveaux et lieu d’enseignement ; tout sigle doit être décliné) :

Depending on the profile of the successful candidate, teaching will take place in one, or more of the following UFRs, at both the undergraduate and graduate levels. Moreover, in collaboration with diiP, the candidate will propose graduate courses on data analytics/intelligence that could be relevant to students from different research fields and disciplines.

UFR Physique:
Master NPAC [Nuclei, Particles, AstroParticles and Cosmology] Master AAIS [Astronomie, astrophysique et ingénierie spatiale]

UFR Linguistique:
Master Linguistique Théorique et Expérimentale
Master Linguistique Informatique
Master in Information Technology (ex-Descartes) https://math-info.u-paris.fr/en/master-in- information-technology/

UFR Mathematiques et Informatique:
Master in Information Technology: Distributed Artificial Intelligence
Master in Information Technology: Computer Vision and Intelligent Machines

Laboratoire de recherche :
Laboratoire Astroparticule et Cosmologie (APC) Laboratoire de Linguistique Formelle (LLF) Laboratoire d’Informatique Paris Descartes (LIPADE)

*Date de prise de fonction
*Date de fin de fonction
*Quotité d’enseignement
*Personnes à contacter
Eric Chassande-Mottin ecm(at)apc.in2p3.fr , Vice-Director of Data Intelligence Institute of Paris (diiP), DR CNRS, AstroParticule & Cosmologie, Université de Paris

Barbara Hemforth bhemforth(ATgmail.com , DR CNRS, Laboratoire de Linguistique Formelle, Université de Paris

Themis Palpanas themis(ATmi.parisdescartes.fr , Director of Data Intelligence Institute of Paris (diiP), Director of LIPADE (Computer Science Department), Université de Paris

 

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