We propose a thesis that will be funded by an Establishment Doctoral Contract from September/October 2022.
Thesis topic: Machine learning approaches applied to the monitoring of energy consumption and environmental data
Director of these: Patrice Wira, [email protected]
Co-supervision: Gilles Hermann, [email protected]
Laboratory: IRIMAS - Institut de Recherche en Informatique, Mathématiques, Automatique et Signal Université de Haute Alsace, Mulhouse, France
I'm sending you 3 job ads in the private sector in IoT and animal health
- https://ns-management.fr/ offres_emplois/manager-method-iot/?fbclid=IwAR1yRseUfW8_uj5Dv9365ql8YnL7BRRbeBj3MdDJwp PFqKLAc3__9PqYkLU
- https://ns-management.fr/ offres_emplois/animal health engineer-f-h/?fbclid=IwAR07qdb7wiaa-lTqZho-g2ZKRJJ_zE3CwhScCv02IAcsvl5v46S51Y72gSM
- https://ns-management.fr/offres_emplois/ux-designer-f-h/?fbclid=IwAR0HFnvRtXsQISKJABHcJedWquwwkzX6mbBl3Ch7Y9J3RZoP0hF2VH_RHw8
The CORLI consortium (https://corli.huma-num.fr/) is recruiting a part-time design engineer on a fixed-term contract for a renewable period of one year .
https://corli.huma-num.fr/fich e-de-poste/
Thank you for sending CV and cover letter to Christophe Parisse [email protected] and Céline Poudat [email protected] before March 31st.
Start of the contract possible from 1 May.
We are in the recruitment phase of an engineer or a Master 2 in the field of Concept Formelle Analysis.
https://www.ast-innovations. com/discover-ast/us-join/engineer-maturation-it-9-month
Areas: Semantic Web, Linked Data, Data linking, Representation learning
Qualifications: PhD in Informatics, AI. Background in knowledge engineering.
Context: ANR DACE-DL (DAta-CEntric AI-driven Data Linking)
Contact & Collaboration: Dan Symeonidou, [email protected] Clement Jonquet, [email protected]
Dates: Position available for 2 years. Beginning date is flexible.
Location: INRAE, Centre Occitanie-Montpellier, MISTEA research unit
Salary: Between 2200€ and 2700€ gross monthly depending on qualifications and situation.
Institut: INRAE is the French research organization in agriculture, food and environmental sciences; it is a pioneer in France in terms of data sharing and Open Science commitment. The MathNum research department gathers around 200 scientists in mathematics and digital technologies in 13 research units in France. MISTEA is a joint research unit of INRAE and Montpellier Institut Agro engineering school with activities in the development of mathematical, statistical and informatics methods dedicated to analysis and decision support for agronomy and environment. The team is also recognized for its expertise in knowledge engineering and ontology-based scientific data management and information systems.
Project context: Data linking is the scientific challenge of automatically establishing typed links between the entities of two or more structured datasets. A variety of complex data linking systems exists, evaluated on public benchmarks [1,2,3]. While they have allowed for the generation of vast amounts of linked data in the context of various dedicated projects, data generic systems often have limited applicability in many real-world scenarios, where data are highly heterogeneous and domain-specific. The ANR project DACE-DL (2022-2024) targets a paradigm shift in the data linking field with a data-centric bottom-up methodology relying on machine learning and representation learning models [4]. We hypothesize there exists a finite number of identifiable and generalisable linking problem types (LPTs), that we need to categorize and analyze to provide better linking results.
Topic: The postdoc will work to identify and provide a categorisation/taxonomy of the different linking problem types based on an in-depth analysis of the linked datasets provided by the project and beyond. The first objective is to provide an in-depth analysis of the linked data available along with an exhaustive study of the state-of-the-art in the field of data linking. A finite number of generalisable linking problem types will be classified including the relations and inherent structure of the LPTs made explicit to both human and machine. The goal is to answer questions such as: are certain LPTs or groups of LPTs (e.g. siblings at a given level of the taxonomy) specific to a domain, language or a community? Are certain LPTs inherent to specific types of data? Once a formal taxonomy of LPTs is produced, various datasets will be manually annotated. These annotations on existing pairs of datasets will be used to learn, using machine learning strategies, features for the automatic categorization of other datasets. The postdoc will co-supervise a PhD student working on the machine learning methods.
Application: Send application to the contact emails including: a short description of introducing yourself your adequacy to the position a CV and one major publication
I am looking to recruit a post-doctoral fellow or a research engineer for 18 months on heterogeneous data fusion themes for the training of deep neural networks.
Funding is already available, and the position is to be filled now (if research engineer) or on 30/04/2022 (if post-doctoral fellow, legal deadline for publication).
The position is at IMT Atlantique, on the Brest campus, France, in a team specialized in machine learning.
All the information at this link >> https://institutminestelecom.r ecruitee.com/l/en/o/postdoctor health-or-postdoctoral-in-fusion-of-heterogeneous-data-for-training-of-deep-learning-models-cdd-18-months
In the context of the development of the ACSS institute, PSL university is hiring two junior and one senior data-scientists. They will be responsible for implementing strategies, tools and methods for producing and analyzing data from various sources (Web, institutional databases, archives, etc.). They will also be responsible for ensuring compliance with best practices in matter of code and data management. Finally, they will contribute to the development of statistical analysis or machine learning algorithms (particularly in the field of natural language processing).
Created by the University Paris Sciences and Letters (PSL) and hosted by Paris-Dauphine, the «Applied Computational Social Sciences» Institute aims to strengthen research on major societal issues (political and social cohesion, ecological transition, digital transformation, economic efficiency and competitiveness) by linking data sciences and social sciences.
The Institute collects and processes heterogeneous data on a large scale both to enable scientific advances and to help inform public dialog and decision-making.
It brings together a multidisciplinary team of researchers and relies on a team of engineers in data science who bring their expertise to build original databases and perform sophisticated analysis. These projects are initiated and supported by research units affiliated to the French National Institute for Research (CNRS) and a group of outstanding research universities. The outcomes of the Institute activities are also featured to reach policymaking and business audiences.
Application: The application form is available at this address: https://acss-dig.psl. eu/candidate. It should include a CV, a cover letter, and a transcript of concluded university degrees. Review of applications will begin April 4th, 2022 and continue until the positions are closed.
A fixed-term contract as a computer engineer in data research and visualization is open at the Université Savoie Mont Blanc to work as part of the development of the virtual campus of the European University UNITA.
Application deadline: March 31
More information in the attached job description.
https://espaces-collaboratifs.grenet.fr/share/s/_n20NbVmQF2ECkKGcl7QMg
We send you a funded thesis offer (doctoral stipend) in Information and Communication Sciences with a computer coloring, within the TECHNE laboratory (Poitiers)
This is part of the scope of the ANR COMPER project, and aims to work on the self-regulatory processes of students in synchronous hybrid learning. For more information see: Description of the thesis
Start date of the thesis: September 1, 2022
To apply, please send to [email protected] and hassina.el.kechai@univ-poitier s.fr: - A CV - Your Transcripts of the Master - A cover letter.
The deadline to apply is April 14, 2022
Selected candidates will be auditioned on the thesis project between April 14 and May 1, 2022 (The precise date will be communicated after selection).
The Image Science and Computer Vision team of Hubert Curien laboratory (https://laboratoirehubertcurien.univ-st-etienne.fr/en/index.html) is looking for candidates for a Ph.D position on “Investigating the contribution of lighting and mixed reality for improving visual perception of individuals with low vision”.
Concurrent with the increase in the average age of people, growth in the number of persons with low vision is unfortunately expected. Low vision can seriously affect the ability to perform simple activities of everyday life as walking safely even at home, pouring water in a glass or easily finding familiar and household objects. The quality of life can be dramatically altered and it can be impossible to maintain independence in a safe manner. Yet loss of independence is a predominant concern of the older adult.
All recent studies highlight a clear lack of efficiency and practicality of visual aids available on the market or developed in laboratories. To respond effectively to the societal challenge of assisting visually impaired people in their everyday lives, new research investigations that break with the existing one must be explored. The aim of this PhD Thesis project is to exploit the potential offered by light combined with digital tools, such as AI and extended reality (XR), for designing efficient visual aids in order to preserve the autonomy of visually impaired people.
The research work that will be carried out aims to develop predictive models for controlling smart adaptive lighting systems to change the environmental cues with the purpose to selectively enhance the residual vision of visually impaired individuals. The underlying idea is to dynamically adapt the light characteristics and its 3D spatial distribution to the visual context. The assistive technologies must adapt to the user needs and expectations as far as possible to optimize the observation conditions of a scene for improving object detection, tracking and classification.
Visual perception, data processing and modelling will be the core elements of this research work. Artificial intelligence / deep learning approaches will be used for the modeling part. Experimental studies will be also at the heart of this PhD with three major assets: - the expertise acquired over the past 5 years by the supervisors and their international partners (in Japan and Thailand) in the management of visual protocols involving human observers ; - the amount of data already collected ; - a platform combining state-of-the-art devices in mixed/augmented reality, image capture, display and measurement systems, as well as a multispectral lighting system unique in Europe.
The thesis will be co-supervised by Alain Trémeau (Full Professor) and Eric Dinet (Associate Professor) and Philippe Colantoni (Associate Professor).
The deadline for applications is 15/04/2022.
The desired profile is Master (MSc or equivalent) or Engineer degree in “Image Processing and Computer Vision” or “Virtual, Augmented and Mixed reality” or “Computer Science and Applied Mathematics” or “Intelligent Systems and Data Mining”, with excellent academic records and research experience, in-depth knowledge of “Data mining, optimization, modeling and simulation methods” and/or machine learning (Computational Neural Networks, Deep Learning), with a specialization in one of the following areas: computer vision, human vision, color, data mining or machine learning.
We are looking for a wiling and keen student with excellent skills in programming (e.g., in Matlab, Python, or C/C++) and software environments (e.g. SDK, HMI), with an interest for (or knowledge in) visual experiments with XR devices (Varjo XR-3, HP Reverb G2, etc.), visual perception and assistive systems for visually impaired people. Good English communication skills and reporting, autonomy and curiosity, sense of initiative and rigour will be greatly appreciated.
Interested candidates should send a CV, a motivation letter, and transcripts of BSc and MSc (M1 and M2 years). Recommendation letters will be appreciated.
All applications must be sent electronically to Alain Trémeau ([email protected]), Eric Dinet ([email protected]) and Philippe Colantoni ([email protected]).
Contract 3-years contract on the basis of a monthly gross income of 1.975 euros approximatively. Part-time teaching can be considered. Start in autumn 2022, preferably in October.
The advent of deep learning has been a real tsunami in the machine learning community, leading to results, especially in computer vision, that we would not have expected a few years before. For many vision tasks, the performances of deep learning algorithms have become equivalent or even superior to human performances.
However, these results have been obtained at the cost of ever-increasing use of resources such as: the size of the model, the time and energy needed to train them, with ever-larger databases and ever-higher annotation requirements. This increase in resource requirements has major drawbacks, related to the impact that ML has on the environment, the difficulty to implement models on embedded architectures, or the challenges raised when models have to be trained on tasks for which little training data is available.
These observations have very recently led some authors (Chen, Zaharia, and Zou, 2020; Evchenko et al., 2021) to introduce the concept of frugal machine learning and to define what a frugal machine learning methodology should be, and how to evaluate frugality.
In this dissertation, we will study frugality in the context of AI for image segmentation (Minaee et al., 2021). The objective will be to propose frugal models that can provide efficient results while being structured to provide a reduced time and space complexity. More precisely, we will consider several aspects of frugality and take inspiration from the following recent works: i) the conception of lightweight models by design (Wang et al., 2021; Xie et al., 2021) ii) the compression of existing models (Eo, Kang, and Rhee, 2021) iii) the pruning of existing segmentation models (He et al., 2021; Wang et al., 2021) iv) frugality on image label and zero shot image segmentation (Bucher et al., 2019; Xu et al., 2021).
Candidates must have an MSc or engineering degree in a field related to computer science, electrical engineering, or applied mathematics, with strong programming skills (in particular with deep learning frameworks). Experience with image processing will be a plus. Candidates are expected to have abilities to write scientific reports and communicate research results at conferences in English.
The position is starting as soon as possible with a salary of 32 kEuros gross, and will be located in Caen, France.
Applications should include the following documents in electronic format: i) A short motivation letter stating why you are interested in this project, ii) A detailed CV describing your past research background related to the position iii) The transcripts for master degrees. iv) The contact information for three references (do not include the reference letters with your applications as we will only ask for the reference letters for short-listed candidates).
Please send your application package to [email protected] and [email protected]
Ideally located in the heart of Normandy, two hours from Paris and just 10 minutes away from the beaches, Caen, William the Conqueror’s hometown, is a lively and dynamic city.
We offer a thesis scholarship in co-supervision between ADAPT (Centre for AI-Driven Digital Content Technology, Dublin) and the CERES unit (Centre d'Expérimentations pour les Méthodes Numériques pour les Sciences Humaines, Sorbonne Université Paris).
The thesis will focus on the use of NLP and learning methods for research in the humanities and social sciences. Particular interest is placed on the study of the added value that these tools offer to SHS research and in return how typical SHS questions can feed the epistemology of computer science.
Candidates, holders of a master's degree in Computer Science or a master's degree in Human Sciences with experience in programming, are invited to send a CV and a cover letter (in French or English) jointly to Mohammed Hasanuzzaman (mohammed.hasanuzzaman@adaptce ntre.ie) and Gaël Lejeune (gael.lejeune@sorbonne-univers ite.fr).