Hello, The Montpellier laboratories LIRMM, IRD, DIPRALANG and MARBEC offer several subjects for internships at the Research Master level for a period of 6 months.
Model language acquisition in children (LIRMM and DIPRALANG)
Development of text mining approaches to identify gene-phenotype interactions (LIRMM et IRD-Occitanie)
Deciphering space-time jellyfish diversity blooms in the Mediterranean Sea through text mining (LIRMM et MARBEC)
These courses are presented in detail on the web page http://www.lirmm.fr/~aze/stages/
All the information to apply is indicated in the internship subjects.
Regards Sandra Bringay and Jérôme Azé
The Mathematical Thinking Initiative within the Priority Research Centre on Computer Assisted Research Mathematics and Applications (CARMA) at the University of Newcastle, Australia, offers PhD supervision on a range of topics related to computer assisted proofs using tools such as Lean, machine learning, mathematics, and cognition. More specific topics include:
Translation of arguments concerning topological groups into Lean and their implementation as minimisation algorithms.
Machine learning and automated mathematical verification using Lean
Number theory and Lean
Mathematical psychology and mathematical thinking
Teaching of critical thinking in mathematics
Teaching of mathematics for social justice
Mathematical modelling and optimisation
Participating academics are: Prof George Willis, A/Prof Stephan Chalup, Prof Florian Breuer, A/Prof Ami Eidels, A/Prof Elena Prieto-Rodriguez, Dr. Hamish Waterer and others.
Students with outstanding track record who are aiming to start a PhD are encouraged to contact the PRC CARMA by 4 November 2021 with a brief EOI (preferred topic area, resume, list of publications) to be shortlisted for scholarship nomination by one of the participating academics. Nominated candidates will then be asked to apply formally for an Australian Government Research Training Program Scholarship – Academic Pathway Scheme (Vice Chancellor Scholarship).
Domestic and international students can apply
All communications in our program are in English
You will join a high-profile research team as a doctoral student and conduct focused research
The VC scholarship includes opportunities for some academic training experience beyond the research experience.
Candidates with background in mathematics, machine learning, physics, neuroscience, control theory, computer science, material science, artificial intelligence, deep learning, or related fields are encouraged to apply.
CARMA is committed to widening participation, promoting diversity and fairness, and overcoming injustice.
Please submit your EOI for nomination to PRC CARMA by 4 November 2021 using the following email [email protected] or contact one of the other above listed academics directly.
PhD Scholarship: Machine Learning for Grinding Mill Design Optimisation This project aims to apply machine learning in combination with programmable mechanical design modelling software to automatically optimise grinding mill geometries for desired operational outcomes. The machine learning component will address deep reinforcement learning as an innovative technology in this application domain. The project will be conducted in close collaboration with industry partner Bradken in Newcastle, Australia.
The PhD candidate will be part of an interdisciplinary team of researchers from engineering, computer science and industrial researchers. The candidate should have good programming skills, knowledge in machine learning and engineering judgement. The project will require design and training of deep neural networks where existing libraries can be combined with new implementations. One of the core techniques is reinforcement learning. The PhD project is part of ARC LP190100378 https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP190100378
Scholarship details: https://www.newcastle.edu.au/study/research/phd-scholarships/phd-scholarships/machine-learning-for-grinding-mill-design-optimisation
Contact: Associate Professor Stephan Chalup
Deputy Director, Priority Research Centre for Computer-Assisted Research Mathematics and Its Applications (CARMA)
School of Information and Physical Sciences
The University of Newcastle Callaghan NSW 2308, Australia
https://www.newcastle.edu.au/profile/stephan-chalup
Dear colleagues, University Milano-Bicocca is offering 2 postdoctoral positions and 1 research assistant on machine learning and AI applied to understand and contrast social media threats for teenagers.
The successful participants will be involved in the multidisciplinary project COURAGE funded by volkswagen foundation. The positions are research only starting in December 2021.
2 post-doc and 1 research assistant (2 years)
Located at Milan-Bicocca but smart working allowed
Informal description https://sites.google.com/site/dimitriognibenehomepage/jobs
Official call https://www.unimib.it/sites/default/files/Ufficio_Bandi/21A112_-_Bando_Assegni_tipo_B.req_._obbl_-_English.pdf
Application page: https://pica.cineca.it/unimib/21a112/
Call website [Ita] https://www.unimib.it/ateneo/gare-e-concorsi/cod-21a11
Project: COURAGE (funded by volkswagen foundation, minimum deliverables effort)
PI Dimitri Ognibene [email protected]
Application Closes: 28/10/2021
Position Starts: 1 December 2021
Hello. In PJ a post-doctorate offer at CEA LIST in Saclay (Université Paris Saclay) on the recognition of materials from uncertain atomic proportions.
Regards, Jean-Philippe Poli
Research engineer
Atomic Energy and Alternative Energies Commission
List Institute | CEA Saclay Digiteo Labs | Build. 565-PC192
F-91191 Gif-sur-Yvette Cedex
T. +33 1 69 08 78 56
M2 Internship - Reading Comprehension For Multimedia Question Answering
*Keywords*: visual question answering, natural language processing, computer vision, machine learning, artificial intelligence.
Context The internship takes place in the framework of the MEERQAT project which focuses on Multimedia Question Answering (MQA). This task consists in answering questions grounded in a visual context.
For instance, while watching a film, one can wonder /“In which movie did I already see this actress?”/ or /‘‘How many Oscar did she won?’’/. It is related to Visual Question Answering (VQA).
However, VQA questions relate to the content of the image, such as the color of an object or the number of objects (e.g. ‘/‘What color is her dress?/''), while MQA focuses on finding answers in text, but with the help of images associated with the questions.
Research problem Question Answering is usually split into two steps: Information Retrieval for selecting a restricted set of documents or passages from a large collection of documents and Reading Comprehension for extracting answers to the questions in the retrieved documents.
The internship will focus on the second step, relying on the work already done in the MEERQAT project for the multimedia retrieval of documents. See the online offer for more details
https://www.meerqat.fr/wp-content/uploads/2021/10/2022_meerqat_internship_mqa-optim.pdf.
Objectives The main objective of the internship is to define, implement, and evaluate methods, in the context of MQA, for taking into account the information brought by images in the Reading Comprehension task. Two main research directions will be considered in this context:
- a late fusion approach relying on the results of the multimedia Information Retrieval step to rerank candidate answers with respect to images;
- a more early fusion approach integrating images in the reader to allow contextual disambiguation.
Internship conditions The internship will be supervised by Paul Lerner along with Olivier Ferret and Camille Guinaudeau and will take place at LISN. LISN is an interdisciplinary laboratory resulting from the merge of LIMSI and LRI in 2021.
It is associated with CNRS and Université Paris-Saclay and includes 16 research teams and 380 people. The intern will be located at /bât 507, Rue du Belvedère, F-91405 Orsay cedex/.
- Remuneration: around 600€ along with the refund of half the Navigo (public transport) card.
- Starting date: the internship is expected to start from March 2022 but could begin earlier.
- Duration: 5-6 months.
Requirements We are looking for an M2 student in Natural Language Processing, Computer Vision or Machine Learning.The intern is expected to be proficient in programming, especially in the Python language, and to have already worked under Linux. They should also have experience with a deep learning framework, preferably PyTorch.
Application Please send a resume along with a cover letter (in French or English) and grade transcripts for the last two years to Paul Lerner at [email protected]. Examples of projects (e.g. via GitHub) is a plus.
The Sonos Voice Experience team in Paris is looking for a freelance Data Annotation Specialist for American English.
Mission Scope: The mission involves annotating speech data to improve our Speech Recognition machine learning models:
- Listen to voice samples and classify them
- Review and update the text of the voice samples
- Document and report issues to improve our data annotation process
This mission does not involve phonemic transcription.
Your contact point will be the Sonos Voice Experience Language Team, based in Paris and Boston.
Skills you’ll need:
Basic Qualifications: - Perfect proficiency in English (native speaker or very advanced C2 level, equivalent to a native speaker level), with excellent spelling and ability to understand English spoken in noisy environments
- Attention to detail, and ability to focus on tasks requiring a high degree of precision and meticulousness
- Ability to quickly learn how to use new technical tools
Preferred Qualifications: - Experience working on data annotation tasks (especially for Speech Recognition), or experience in copy editing
- Experience working with data annotation platforms/tools
- Passion for music, and extensive knowledge of musicians/artists
Status and duration: Freelance mission. Freelance status required (auto-entrepreneur or other).
Initial duration of 2 months, with a possible extension. We expect you to work on this mission 2 to 3 days a week during this period.
Location: France, home-based.
You’ll work on your own computer - your computer must be recent enough to be able to run a quite heavy annotation tool.
How to apply:
Send your CV and cover letter to Odile de Vismes, Linguistic Resources Manager:
Hello. In PJ, you will find an M2 internship offer with a potential thesis follow-up.
Thanks for the broadcast.
Keywords: time series, fuzzy logic, interpretability, causality JP Polished
Post-doctoral position: Deep Learning for opinion mining in human testimonials related to industrial accident
The Machine Learning team at the LITIS laboratory, the computer science laboratory of the University of Rouen Normandy, is looking for a post-doctoral researcher on a 18-months contract, starting as soon as possible.
The position will be financed by the ANR research project CATCH (french acronym for "Automatic Understanding of Human Sensors Testimonials"), which involves the R&D center of the company Saagie, specialized in B2B DataOps solutions, Atmo Normandie, one of the approved French air quality monitoring associations and LITIS.
Location : LITIS lab., University of Rouen Normandy, Rouen, France
Duration : 18-months, starting as soon as possible
Salary : ~2300€ / month (before income tax), including social security coverage in line with French regulations
Applications : open from 01/09/2021 to 31/12/2021
Keywords: Deep learning – Natural Langage Processing – Sentiment Analysis / Opinion Mining
Scientific context: The ambition of the CATCH project is to propose artificial intelligence and deep learning tools to take into account and automatically exploit the multitude of human testimonies related to an industrial accident and its consequences on the environment and health. By involving the population in the collection and analysis of data, particularly through social networks, and by providing effective means for interpreting this data, the proposed solution should contribute to providing answers to the worrying problem of industrial accidents and their consequences.
The overall objective is first to draw up a precise cartography of the nuisances in order to follow the propagation and the evolution of the phenomena in time, and then to analyze and characterize the sentiment of the population and its evolution throughout the crisis. To do so, we intend to exploit testimonials collected on the ODO platform of Atmo Normandie, which combines these testimonies with geographical information, in conjonction with data extracted from the micro-blogging platform Twitter. Since these data are primarily texts, state-of-the-art approaches from the Natural Language Processing (NLP) field are favored, in particular, self-supervised deep learning methods such as Transformers that are known to be the most performant today for a wide range of NLP tasks.
Research goals: The objective of this research work is twofold: 1. The automatic generation of a map of nuisances around the site of an industrial incident to monitor the propagation and the evolution of the phenomena over time.
2. The automatic recognition of the population's perception and its evolution throughout the crisis.
Related to these tasks, the post-doctoral researcher will be in charge of proposing solution for:
• extracting and linking twitter data with testimonials from the ODO dataset, which is fully labelled and associates textual testimonies with geographical data. The interest in establishing this link is to be able to enrich the data from the ODO platform to refine the mapping of nuisances in real time. This could be achieved for example, by using pseudo-labelling techniques1 or Constrative Representation Learning methods which have recently been applied to text data2.
• detecting in all the testimonials collected from Twitter or from the ODO platform, the presence (or absence) of several pre-identified emotions (e.g. surprise, fear, anger, sadness, disgust, etc.), several of which can be expressed at the same time.
This research work will therefore involve being familiar with the state-of-the-art NLP deep learning methods and in particular with their applications to sentiment analysis and opinion mining tasks. It will also require experience with the use and exploitation of data from Twitter in a data science context.
Application: The successful applicant will: • possess or be in the process of obtaining a Ph.D. in computer science or applied mathematics, with a focus on machine learning or data mining.
• have strong programming skills (Java, Python, etc.) and in-depth understanding of statistics and machine learning.
• have already worked with deep learning architecture dedicated to texts (RNNs, Transformers, etc.) and/or images (CNNs, FCNs, GANs). • have a productive publication record.
Your application should include: • curriculum vitae
• statement of past research accomplishments, career goal and how this position will help you achieve your goals
• two representative publications
• contact information for at least two references
Contact Application must be sent to : • Simon BERNARD, University of Rouen Normandy, [email protected]
• Clément CHATELAIN, INSA Rouen Normandy, [email protected]
• Alexandre PAUCHET, INSA Rouen Normandy, [email protected]
Best regards -- Simon Bernard LITIS / NormaSTIC FR CNRS 3638
Université de Rouen Normandie, France http://pagesperso.litislab.fr/sbernard/
Researcher/Ph.D. position in UAS LIDAR
https://euraxess.ec.europa.eu/jobs/693484
As part of the ANR PICTURE project ( https://picture-anr.cea.fr ) on the safety of on-board neural network models and bringing together CEA LETI, the Ecole des Mines de Saint-Etienne (MSE), STMicroelectronics and IDEMIA, MSE offers an 18-month post-doctoral fellowship on model security against physical attacks.
The position will take place at the Provence Microelectronics Center (Gardanne) in the SAS joint research team between MSE and CEA LETI.
Contacts: Jean-Max Jean-Max DUTERTRE: [email protected]
Olivier POTIN: [email protected]
Pierre-Alain MOELLIC: [email protected]
More information on the attached job description.
Objectives The post-doctoral position is directly related to the evaluation activities that will be focused on the assessment of the robustness of the models and theirs protections against physical attacks, more particularly the fault injection threats [6, 7, 8].
The post-doctoral researcher will have access to cutting edges equipment at the Centre de Microélectronique Provence – CMP – and the MicroPacks platform : side-channel analysis, laser impulsion (with on double-spot IR laser) and electromagnetic injection tools.
The post-doctoral researcher will work within a team composed of physical security and security of Machine Learning experts as well as two PhD students (CEA-LETI) and one post-doctoral researcher (CEA-LETI) gathered within the Joint Research Team between CEA-LETI and MSE at the CMP.
Skills This position requires applicants to be PhD holders with skills and experiences on embedded systems (C++, assembler, compilation).
A first experience on embedded neural networks is an advantage for the position (STM32Cube.AI, Tensorflow-lite...). Knowledge about physical attacks and/or the RISC-V platform will be appreciate.
Embedded Systems (good knowledge of microcontroller architecture)
Physical security
Machine Learning / Deep Learning: at least the basics of deep neural networks.
Application Application must be sent to Jean-Max Dutertre, Olivier Potin and Pierre-Alain Moellic : [email protected], [email protected] , [email protected].
An application gathers detailed resume, motivation letter and potential recommendations.