RESEARCH FIELDPhysics › Solid state physics
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE26/02/2021 23:00 - Europe/Brussels
LOCATIONFrance › Paris
TYPE OF CONTRACTTemporary
HOURS PER WEEK35
OFFER STARTING DATE01/09/2021
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions COFUND
MARIE CURIE GRANT AGREEMENT NUMBER945304
“Artificial intelligence for the Sciences” (AI4theSciences) is an innovative, interdisciplinary and intersectoral PhD programme, led by Université Paris Sciences et Lettres (PSL) and co-funded by the European Commission. Supported by the European innovation and research programme Horizon 2020-Marie Sklodowska-Curie Actions, AI4theSciences is uniquely shaped to train a new generation of researchers at the highest academic level in their main discipline (Physics, Engineering, Biology, Human and Social Sciences) and master the latest technologies in Artificial Intelligence and Machine Learning which apply in their own field.
26 doctoral students will join the PSL university's doctoral schools in 2 academic cohorts to carry out work on subjects suggested and defined by PSL's scientific community. The 2020 call will offer up to 15 PhD positions on 24 PhD research projects. The candidates will be recruited through HR processes of high standard, based on transparency, equal opportunities and excellence.
Description of the PhD subject: “Towards neuromorphic computing on quantum many-body architectures”
Context - Motivation
Artificial intelligence (AI) algorithms today are coded into silicon based computer architectures. However, the promise of AI cannot reach its full potential until the underlying architectures more closely resemble the synapses and neurons of the brain. While many materials are considered candidates for mimicking synapses, only a handful of materials, all quantum many-body systems, are considered viable for mimicking neurons. In these materials, electrons clump into richly textured shapes. These multiscale structures likely hold the key to neuron-like properties as the materials become highly susceptible to stimuli like electrical spikes in brains. To understand these multiscale structures we propose combining for the first time three high spatial and temporal resolution surface probes [optical microscope, scanning near-field optical microscopy (sNOM), and scanning tunneling microscopy (STM)] and robust image analysis based on new machine learning (ML) and statistical mechanics theoretical methods.
This full experimental/theoretical approach will help us to characterize and learn to control the properties of these neuromorphic quantum materials in order to facilitate their eventual incorporation into neuromorphic computer architectures while advancing the field of quantum many-body systems.
This is an international collaboration between the supervisors’s experimental research group and the co-supervisor’s theoretical research group. The theoretical techniques including image processing, cluster analysis techniques, machine learning techniques, and resistor network methods. They will be applied to interpret detailed spatially resolved experimental data from optical microscope movies, sNOM, and possibly STM applied to the candidate “neuristor” materials La0.7Ca0.3MnO3, VO2, V2O3 and NdNiO3.
Over the last three years, the ESPCI - PSL in Paris has set up a nanoscale platform unique in France to characterize quantum materials. This project will use two state of the art probes in this platform: a low temperature STM and a Linkam self-focusing optical microscope. The first allows surface sample mapping at atomic resolution on metal and insulating materials using an atomic force microcope (AFM) position tip mode. The second allows micron resolution image movies at 100ms intervals to probe the rich temporal dynamics in these neuromorphic quantum materials. These will be complemented by sNOM measurements with 20nm resolution, through an ongoing ICAM/I2CAM collaboration with Dmitri Basovs’s group at Columbia University. By overlapping the datasets of all three probes, we will map out for the first time six decades of spatial range (0.1nm to 100microns) in these neuromorphic materials. These multirange maps will permit not only (i) static fine structure analysis in the image analysis process but also the identification of (ii) dynamic fine changes when these quantum materials are placed in neuromorphic circuits which use electrical spike signal propagation.
Scientific Objectives, Methodology & Expected results
We use computational techniques to predict expected
- Static spatial patterns that can arise in these materials from various candidate models. That, in turn, feeds into both cluster analysis techniques and our machine learning (ML) algorithms. Many of the materials form intricate structures with fractal boundaries and interiors, and the cluster analysis techniques quantify these and other geometric measures to compare with theoretical models. The ML algorithms do not require this intermediate step of interpretation. Rather, we use simulations to predict thousands of potential patterns from each type of model and, under supervised learning conditions, we use those patterns to train a convolutional neural network to recognize which model generated which image. Once trained, the neural network will be applied to images derived from experimental data. The combination of these two complementary theoretical techniques will allow us to develop detailed models of the fundamental physics driving the intricate pattern formation in these quantum materials.
- Samples will then be placed in a full neuromorphic architecture where electrical spikes pass across. Fast optical microscopy with simultaneous resistance measurements will allow us to capture the dynamics of the metal/insulator patterns. A combination of cluster analysis and ML analysis mentioned above with a resistor network analysis will be used to identify any fine changes in the fractal pattern formation. This will allow us for the first time to visualize and follow the creation of percolation paths, giving us the opportunity to control the properties of these neuromorphic quantum materials.
The ultimate goal of our research is to realize the potential of quantum materials to build disruptive computer architectures capable of mimicking the brain, including synapses and neurons. While many materials are considered candidates for mimicking synapses, only a handful of materials are considered viable for mimicking neurons. In this project, we will combine optical experimental techniques with AI and informatics theoretical methods to characterize and learn to control the properties of four of these materials, VO2, V2O3, NdNiO3, and LCMO, in order to facilitate their eventual incorporation into neuromorphic computers.
The PhD candidate will be working on experimental setups and data treatment using machine learning. A trip to Purdue University (IN, USA) in the team of Prof. Erica W. Carlson will be planned during this project. The travel schedule and length will be determined according to experimental work progress and data available.
Lionel Aigouy and Alexandre Zimmers
Created in 2012, Université PSL is aiming at developing interdisciplinary training programmes and science projects of excellence within its members. Its 140 laboratories and 2,900 researchers carry out high-level disciplinary research, both fundamental and applied, fostering a strong interdisciplinary approach. The scope of Université PSL covers all areas of knowledge and creation (Sciences, Humanities and Social Science, Engineering, the Arts). Its eleven component schools gather 17,000 students and have won more than 200 ERC. PSL has been ranked 36th in the 2020 Shanghai ranking (ARWU).
- PhD project, subject to the availability of funding -
- Opportunity to conduct academic research in a top 100 university in the world.
- High-quality doctoral training rewarded by a PhD degree, prepared within ESPCI - PSL and delivered by PSL.
- Access to cutting-edge infrastructures for research & innovation.
- Appointment for a period of 36 months (job contract delivered by the involved component school of PSL) based on a salary of 3100 € gross employer (including employer tax) per month or approximately a 2228 € gross salary per month.
- Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year (“congés annuels”). Eventual complementary activities may be accepted or proposed by the co-supervisors (maximum of 64h/year for teaching, 32 day/year for specific missions).
- Short stay(s) or secondment in France or abroad are expected.
- An international environment supported by the adherence to the European Charter & Code.
- Access to AI training package, with a strong interdisciplinary focus, together with a Career development Plan.
- Applicants must have a Master’s degree (or be in the process of obtaining one) or have a University degree equivalent to a European Master’s (5-year duration) to be eligible at the time of the deadline of the relative call.
- There is no nationality or age criteria, but applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately before the deadline of the call (MSCA Mobility rule).
- Applicants must declare to be available to start the programme on schedule.
For submitting your online application, go to: https://www.psl.eu/recherche/grands-projets-de-recherche/projets-europee...
The online application should contain the following documents:
- English translated transcripts from the Master’s degree (or equivalent 5-year degree). A copy of the Master’s degree or a certificate of achievement will be required later on for the final registration.
- International curriculum vitae and a cover letter explaining the reasons that lead him/her to prepare a PhD, why he/she applies to this offer and his/her professional project (guidelines will be given to the applicants in order to help him/her in the writing of his/her letter).
- Two academic reference letters.
- A statement duly signed on the mobility rules, availability, and conflicts of interest.
The applicants can only apply to one PhD project among the available ones. Multiple applications of one candidate will automatically make all his/her applications ineligible.
The applications will be analysed by the Management Team for eligibility and completeness. Afterwards, the applications will be reviewed by the Selection Committee. In the pre-selection round (March-April 2021), applicants will be rated using a scoring system based on 3 criteria (academic excellence, experience, motivation, and qualities). A shortlist of qualified applicants will be interviewed during the selection round (June 2021) to further assess their qualifications and skills according to the predefined selection criteria.
All information regarding the applications (criteria, composition of the Selection Committee, requirements) can be found on the website of the programme, in greater detail.
The selection and recruitment processes of the PhD student will be in accordance with the European Charter for Researchers and Code of Conduct of the Recruitment of Researchers. The recruitment process will be open, transparent, impartial, equitable, and merit based. There will be no discrimination based on race, gender, sexual orientation, religion of belief, disability, or age.
The Laboratoire de Physique et d'Etude des Matériaux (LPEM) is a joint research unit (UMR 8213) at ESPCI - PSL, CNRS and Sorbonne Université. The main research topics are: Nanophysics, Nanostructures and Nanomaterials; Strongly correlated and low dimensionality electronic systems; Technical instrumentation.
Over recent years, A. Zimmers and L. Aigouy have studied metal-insulator transitions by optical spectroscopy and local probes (AFM, STM, optical microscopy, thermal mapping). These studies have not only shed light on the fundamental mechanism responsible for these transitions but also have direct application in optoelectronics (micro-bolometers, smart windows, neuromorphic circuits…).
ESPCI - PSL is a leading French “Grande Ecole” founded in 1882, part of Université PSL, educating undergraduate and graduate students through a programme merging basic science and engineering, as well as a world-renowned research institution. ESPCI - PSL has setup a tradition of excellence in research, with distinguished faculty that have contributed to its history, such as Pierre and Marie Curie, Paul Langevin, Frédéric Joliot-Curie, Pierre-Gilles de Gennes and Georges Charpak. The five Nobel laureates in this list are emblematic of the exceptional ethos embodied in the permanent culture of excellence at ESPCI Paris.
ESPCI - PSL hosts 11 research units, all associated to CNRS and/or INSERM and/or other Parisian Universities in the form of joined research units, covering the fields of physics, chemistry and biology. Favouring interdisciplinary and operating at the frontiers between fundamental research and innovation, are two major objectives of ESPCI - PSL. This is achieved through a flexible organisation (without departments) that ensures a cross fertilization between scientific disciplines, as well as a direct connection between basic science and applications. One of ESPCI - PSL’s distinctive features is that it carries out fundamental research into areas of major interest to industry, while developing various approaches to practical industrial problems through the deep, fundamental understanding of the mechanisms at play. Performing fundamental research while keeping an eye on applications enables ESPCI - PSL research scientists to make an impact at multiple levels.
Scientists at ESPCI - PSL publish more than one scientific paper a day, and at the same time apply for one patent a week and create several technology-driven start-ups every year - over the last 10 years. ESPCI - PSL has a long experience in the management of European projects. .
Web site for additional job details
Required Research Experiences
YEARS OF RESEARCH EXPERIENCE1 - 4
REQUIRED EDUCATION LEVELPhysics: Master Degree or equivalent
REQUIRED LANGUAGESENGLISH: Excellent
- Solid academic background in Physics / Computer science.
- Hard-working /passionate in scientific experiments and analysis.
- This PhD project will be the corner stone of the ongoing France/USA collaboration, so therefore the PhD candidate will need to show strong team work abilities and good speaking and writing skills in English.
EURAXESS offer ID: 580775
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