Orange is recruiting a PhD student on the topic "Deep learning for the joint processing of natural language and knowledge".
The objective of the thesis is to propose solutions to pool the processing of natural language understanding and generation tasks.
It will thus be a question of studying the progressive fusion of various tasks mixing natural language and formal language(s) of representation or manipulation of knowledge.
The context of application will first be that of isolated statements, then that of human-machine dialogues where the discussion history must be taken into account.
Details and application via Orange Jobs: https://orange.jobs/jobs/offer.do?joid=111967&lang=EN
Application deadline: April 30, 2022
Thesis or postdoctoral topic as part of the Popcorn project (collaborative project with two companies) supervised by Benjamin Lecouteux, Gilles Sérasset and Didier Schwab (Laboratoire d'Informatique de Grenoble, Groupe d'Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole)
Title: OPérationnel population of knowledge bases and Neural
Networks The project addresses the problem of semi-automated enrichment of a knowledge base through automatic text analysis. In order to achieve a breakthrough innovation in the field of Natural Language Processing (NLP) for security and defense customers, the project focuses on the processing of French (even if the approaches chosen will subsequently be generalizable to other languages).
The work will address different aspects: - The automatic annotation of textual documents by detecting mentions of entities present in the knowledge base and their semantic disambiguation (polysemy, homonymy); - The discovery of new entities (people, organizations, equipment, events, places), their attributes (age of a person, reference number of a piece of equipment, etc.), and relationships between entities (a person works for an organization, people involved in an event, ...).
Particular attention will be given to the fact of being able to adapt flexibly to changes in ontology, taking into account the place of the user and the analyst for the validation/capitalization of the extractions carried out.
The project focuses on the following three research axes: - Generation of textual synthetic data from reference texts; - Recognition of entities of interest, associated attributes and relationships between entities. - Semantic disambiguation of entities (in case of homonymy for example )
Profile sought: - Solid experience in programming & machine learning for Automatic Language Processing (NLP), including deep learning
- Master/PhD Machine Learning or computer science, a TAL or computational linguistics component will be a plus appreciated - Good knowledge of French
Practical details: - Start of the thesis back to school 2022 - Full-time doctoral contract at the LIG (Getalp team) for 3 years (salary: min 1768€ gross monthly) - or Full-time postdoctoral contract at the LIG (Getalp team ) for 20 months (salary: min 2395€ gross monthly)
Scientific environment: The PhD or postdoctoral fellowship will be conducted within the Getalp team of the LIG laboratory (https://lig-getalp.imag.fr/).
The person recruited will be welcomed into the team which offers a stimulating, multinational and pleasant working environment. The means to carry out the (post)doctorate will be ensured both with regard to missions in France and abroad and with regard to equipment (personal computer, access to the GPU servers of the LIG, Jean Zay Calculation Grid of the CNRS).
How do I apply? To apply for a doctoral thesis, candidates must hold a Master's degree in Computer Science, Machine Learning or Natural Language Processing (obtained before the start of the doctoral contract, students currently in Master 2 can thus apply).
To apply for a postdoctoral fellowship, candidates must hold a doctoral thesis in computer science, machine learning or natural language processing (obtained before the start of the doctoral contract, students whose defense is scheduled before the end of September 2022 can thus apply).
They should have a good knowledge of machine learning methods and ideally experience in corpus collection and management. They must also have a good knowledge of the French language .
Applications must contain: CV + cover letter/message + master's notes + letter(s) of recommendations; and be addressed to Benjamin Lecouteux (benjamin.lecouteux@univ-greno ble-alpes.fr, Gilles Sérasset ([email protected] and Didier Schwab (Didier.Schwab@univ-grenoble-a lpes.fr
The Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam (UvA) has a 2-year postdoc position in Semantics: Linguistic Interpretation as Abduction.
The position is part of the NWO Open Competition project 'A Sentence Uttered Makes a World Appear---Natural Language Interpretation as Abductive Model Generation'. The successful applicant will join the group of Reinhard Muskens. The aim of the project is to explore how semantic values are assigned to natural language expressions in a compositional way and how the resulting values, represented as logical expressions, are enriched by means of abductive reasoning. The latter is studied in a tableaux setting.
Further details about the project can be found at the UvA vacancies site:
https://vacatures.uva.nl/UvA/job/Postdoctoral-Researcher-in-Semantics-Linguistic-Interpretation-as-Abduction/745463202/
The deadline for applying is 8 May 2022 (use the "Apply now" link at the site linked to above).
Adaptive human machine interface (HMI) for the recovery of control of the intelligent vehicle in a virtual environment (Human-in-the-Loop) in critical situations.
Indira Thouvenin and Queen Talj https://www.hds.utc.fr/~ithouven/dokuwiki/en/start
HDR Teacher-Researcher - UMR CNRS 7253 Heudiasyc
Sorbonne Universities, University of Technology of Compiègne
Computer Engineering - office 141 57 avenue de Landshut 60203 Compiègne (France)
The Roland Mousnier Center and the CERES service unit are looking for candidates for a thesis project in digital humanities applied to history, "Numerical methods for the renewal of historical demography", the full description of which is given below. under.
The deadline for applications is Thursday, April 21.
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, a cover letter and their master's transcripts jointly
to Isabelle Robin (isabelle.robin@sorbonne-unive rsite.fr),
François-Joseph Ruggiu (francois-joseph.ruggiu@sorbon ne-universite.fr)
and Gaël Lejeune (gael.lejeune@sorbonne-univers ite.fr).
The Computer Science Laboratory of the University of Le Mans (LIUM-EA 4023) offers a thesis on the design and operationalization of collaborative educational activities in virtual reality.
Thank you for spreading this proposal widely around you.
Applications must be sent to Lahcen Oubahssi [email protected] before 20 May 2022. The application must include:
a CV, a letter of motivation,
Master 1 and Master 2 transcripts (those available)
the report of the Master 2 internship (if available) letters of recommendation.
The 3MAH team (Modeling and Mechanical Behavior of Heterogeneous Materials) of the DuMAS department of the I2M in Bordeaux offers a thesis topic on the multi-scale modeling of heterogeneous materials using hybrid models Artificial Intelligence (neural networks) and Physics, and the recognition of microstructures from AI algorithms (2D / 3D).
This thesis is funded on a doctoral contract from the University of Bordeaux via a specific call for Artificial Intelligence (acquired funding).
You will find the description of the subject in the file attached to this announcement.
This thesis will be co-supervised by Etienne Prulière (model reduction expert) and Michael Clément (image recognition algorithm expert) and will start in October 2022.
Do not hesitate to disseminate this information widely around you, and to contact us to apply and / or if you want additional information .
You can apply now by email ([email protected] mailto:yves.chemisky@u-bordea ux.fr ).
We are considering a selection of candidates by the beginning of May.
a thesis offer on a subject mixing machine learning and astrophysics. The thesis will be co-supervised by the CEA-List and the LPNHE (Sorbonne University).
Interested students are invited to contact Aurélien Benoit-Lévy ([email protected]) and Olivier Martineau ([email protected]).
Hello everyone, The DUKe (Data User Knowledge) team of LS2N (Laboratoire des sciences du numérique de Nantes), UMR CNRS 6004 (https://www.ls2n.fr) and chekk (https://www.chekk.me) are launching a call for applications for a CIFRE doctoral position in the field of knowledge graphs and machine learning.
Title: Integration of semantic knowledge into a graph diving approach for improving the quality of knowledge graphs
Keywords: Entity resolution, Knowledge graph, Machine learning, Deep learning, Graph diving, Ontology.
Please find attached a full description of this offer. Do not hesitate to spread it around you.
Mounira Harzallah Maître de Conférences HC HdR - Associate professor https://pagespersowp.ls2n.fr/mouniraharzallah/
Keywords Solar resource, solar photovoltaics, artificial intelligence, machine/deep learning, computer vision.
Required profile You hold a PhD degree in engineering science and you have professional experience of less than three years in the development of software solutions based on machine/deep learning and computer vision.
You master the English language and you are sensitive to energy issues. You are rigorous, organized,
dynamic, and critical, you have the ability to work independently and as a team. You have skills in: artificial intelligence (machine/deep learning, computer vision, etc.); data science;
signal and image processing; Python programming (TensorFlow, PyTorch, OpenCV, etc.); database management (SQL, SQLite, MySQL, PostgreSQL, etc.);
shell/bash/zsh and GNU/Linux administration; Git and CI/CD; putting software solutions into production.
Conditions Temporary contract of 10 months (06/01/2022-03/31/2023), possible permanent contract in SESA thereafter.
Salary: according to experience.
Location: PROMES-CNRS laboratory, rambla de la thermodynamique, Tecnosud, 66100 Perpignan, France.
Contacts Stéphane Thil, [email protected]. Stéphane Grieu, [email protected]. Julien Nou, [email protected]. Jean-Baptiste Beyssac, [email protected].
## HIRING: machine learning research scientist
The Machine Learning Team at the National Institute of Mental Health (NIMH) in Bethesda, MD, has an open position for a machine learning research scientist. The NIMH is the leading federal agency for research on mental disorders and neuroscience, and part of the National Institutes of Health (NIH).
Our mission is to help NIMH scientists use machine learning methods to address research problems in clinical and cognitive psychology and neuroscience. These range from identifying biomarkers for aiding diagnoses to creating and testing models of mental processes in healthy subjects. Our overarching goal is to use machine learning to improve every aspect of the scientific effort, from helping discover or develop theories to generating actionable results.
We work with many different data types, including very large brain imaging datasets from various imaging modalities, behavioral data, and picture and text corpora. We have excellent computational resources, both of our own (tens of high-end GPUs for deep learning, several large servers) and shared within the NIH (a cluster with hundreds of thousands of CPUs, and hundreds of GPUs).
As a machine learning research group, we develop new methods and publish in the main machine learning conferences (e.g. NeurIPS and ICLR), as well as in psychology and neuroscience journals. Many of our problems require devising research approaches that combine imaging and non-imaging data, and leveraging structured knowledge resources (databases, scientific literature, etc) to generate explanations and hypotheses. You can find more about our work and recent publications at
https://cmn.nimh.nih.gov/mlt
We are seeking candidates who are capable of combining machine learning, statistical, and domain-specific computational tools to solve practical data analysis challenges (e.g. designing experiments, generating and testing statistical hypotheses, training and interpreting predictive models, and developing novel models and methods). Additionally, candidates should be capable of visualizing and communicating findings to a broad scientific audience, as well as explaining the details of relevant methods to researchers in a variety of domains.
Required experience: - deep learning, using PyTorch or Tensorflow
Desirable experience: - reinforcement learning - Bayesian statistical modelling - other types of modelling of human/animal learning and decision-making
- neuroimaging data processing/ analysis (any MRI modality, MEG, or EEG) - other types of neural data (e.g. neural recording, calcium imaging)
all in the context of substantial research projects, ideally having led to submitted or published articles.
Finally, you should have demonstrable experience programming in languages currently used in data-intensive, scientific computing, such as Python, MATLAB or R. Experience with handling large datasets in high performance computing settings is also very valuable. Although this position requires a Ph.D. in a STEM discipline, we will consider applicants from a variety of backgrounds, as their research experience is the most important factor. Backgrounds of team members include computer science, statistics, mathematics, and biomedical engineering.
This is an ideal position for someone who wants to establish a research career in method development and applications driven by scientific and clinical needs. Given our access to a variety of collaborators and large or unique datasets, there is ample opportunity to match research interests with novel research problems. We also maintain collaborations outside of the NIH, driven by our own research interests or community impact.
If you would like to be considered for this position, please send [email protected] a CV, with your email serving as cover letter. We especially encourage applications from members of underrepresented groups in the machine learning research community. If you already have a research statement, please feel free to send that as well. There is no need for reference letters at this stage. Other inquiries are also welcome. Thank you for your attention and interest!
PhD Position at INRIA Sophia ANtipolis - Machine Learning / Image Processing
PhD subject Title : Early prediction of cell differentiation
Context : Our collaborators have established an in vitro differentiation protocol of embryonic stem cells (ESCs) to mesodermal cells, with a proportion of them differentiating to early lineage of steroidogenic cells. To study and optimize the conditions of in vitro ESCs differentiation, we have defined a protocol that consists of cell cultures that are imaged at different time points. Our main goal is to correlate the success rate with gene or protein expression profiles. However, the success rate only reaches a few percent and can be currently detected only after several days of culture leading to a time- consuming experimental design.
Supervisor : Xavier Descombes, INRIA ([email protected]) Collaborators : Ioannis Oikonomakos, Charlotte Steenblock (Carl Gustav Carus University Clinic, Dresden, Germany), Andreas Schedl (Valrose Biological Institute, Nice, France).
Dear colleagues, I am Raheleh Jafari a University Academic Fellow in AI technology in Fashion Design at the School of Design, University of Leeds.
I wanted to reach out to see if you could share the job vacancy below with any students (with MSc degrees or PhD degrees) across your networks who may be interested in applying? Thank you.
Research Assistant in Natural Language Processing for Conversational AI
We are looking for graduates from Computer Science background or a similar/related field relevant to the position. You will already have some experience in programming and deep learning and data analysis techniques to develop an AI-based customer service chatbot software to interact with online shoppers. Please note that this is a full-time role, and therefore cannot be undertaken alongside a course of study.
Please see the attached job description for more details. The application is open on AHCDE1177 Research Assistant in Natural Language Processing for Conversational AI - Jobs at the University of Leeds and the closing date is Sunday 24 April 2022.
To explore the post further or for any queries you may have, please contact:
Dr Raheleh Jafari (School of Design) Email: [email protected]
Two funded PhD positions/scholarships are available at the College of Science and Engineering, Flinders University, Australia. The research area is in Machine Learning and Deep Learning. Projects are described below.
Project A: Deep Learning for medical image modality This project will exploit Generative Adversarial Networks (GANs) for the synthesis of Head MRI from Head CT/multi-energy CT scan. We will focus the clinical applications in head stroke detection and diagnosis, traumatic brain injury, and brain cancer.
Project B: Machine Learning for airport baggage inspection Baggage inspection is a measure to prevent the transportation of prohibited and dangerous goods and is a critical element of airport security. There is a growing need in delivering sufficient but more flexible security and ensure a more positive passenger experience. This project will use machine learning and deep learning approaches in processing and analysing 2D and 3D x-ray images for baggage check, thus building a resilient, efficient, and intelligent system for airport security.
Essential and Desirable Criteria: Candidates with a relevant background (e.g. Medical Image Analysis, Computer Vision, Computer Science, ICT and Engineering, Mathematics and Statistics) are invited to apply. Positions are open to Australian and International students.
For project A, working knowledge of deep learning architectures, GANs and python is essential. Working knowledge of Matlab is desirable. Previous research experience with medical images e.g. CT and MRI images is beneficial.
For project B, working knowledge of Computer Vision and Machine Learning in images is essential. Competent in programming languages such as Matlab and python. Previous research experience with CT images is beneficial.
The successful candidate will work under the supervision of Dr Gobert Lee and Dr Mariusz Bajger and must be able to work independently and as part of the group. Collaboration with academic, clinical, and industrial partners will be required. The successful candidate must also demonstrate excellent oral and written communication skills.
The PhD positions/scholarships: Scholarship stipend $28,854 (2022 full-time rate) per year for 3 years (max 3.5 years)
Projects are expected to commence before June, 2022 (commence date maybe negotiable)
For information and to apply, please send email to [email protected]. Application please include: 1) a current CV and your transcripts 2) a cover letter outlining your interest in, and suitability for, this project; and 3) the names and contact details for two academic/industrial referees. Applications will be accepted on a rolling basis.
Best regards, Dr Gobert Lee College of Science and Engineering, Flinders University, Australia https://www.flinders.edu.au/people/gobert.lee
PhD studentship in epistemic artificial intelligence
Eligibility: all students
Bursary: £16,540 per year
Fees: Tuition fees will be paid by the university
Deadline for applying: May 22 2022
Start date: September 2022
The Faculty of Technology Design and Environment at Oxford Brookes University is pleased to offer a three-year full-time PhD studentship to students commencing September 2022, funded by the EU Horizon 2020 project “Epistemic AI”.
The successful candidate will join the Visual Artificial Intelligence Laboratory under the supervision of Professor Fabio Cuzzolin. This is a fully-funded PhD studentship with annual bursary of £16,540.
The Visual Artificial Intelligence Laboratory is a fast-growing research unit currently running on a budget of £3.2 million from nine live projects funded by the EU (2), Innovate UK (2), the Leverhulme Trust and others. Our research interests span artificial intelligence, uncertainty theory, machine learning, computer vision, autonomous driving, surgical and mobile robotics, AI for healthcare. The Lab is currently pioneering frontier topics in AI such as machine theory of mind, self-supervised learning, continual learning and future event prediction.
The PhD students will join the Lab’s work towards a new Horizon 2020 FET (Future Emerging Technologies) project “Epistemic AI” coordinated by Prof Cuzzolin and whose other partners are TU Delft (Netherlands) and KU Leuven (Belgium). The project started in March 2021 and will end in February 2025.
The project’s overarching objective is to develop a new paradigm for a next-generation artificial intelligence providing worst-case guarantees on its predictions thanks to a proper modelling of real-world uncertainties. The project re-imagines AI from the foundations, with the aim of providing a proper treatment of the ‘epistemic’ uncertainty stemming from a machine’s forcibly partial knowledge of the world by means of advanced uncertainty theory. All new algorithms and learning paradigms are to be tested in the context of autonomous driving.
Candidates should have a strong mathematical background, specifically in optimisation, probability and statistics, and a good first degree in Machine Learning, Artificial Intelligence or related fields. Applicants are also expected to have Research experience in Machine Learning or Artificial Intelligence, and good coding skills in Python and/or C++. Knowledge of uncertainty theory, including belief functions, random sets or imprecise probabilities is desirable, as is experience of coding in Torch, PyTorch, Tensorflow or Caffe, and experience of work in autonomous driving.
To apply for this studentship please see the submission instructions on our website:
https://sites.google.com/brookes.ac.uk/tde-research/studentships-how-to-apply? authuser=0
When completing your application online, please note the following: Title: PhD studentship in epistemic artificial intelligence
Select the following course: MPhil/ PhD in Computing
Applications must be completed by 5pm on March 22 2022.
Enquiries: Dominic Maitland: dmaitland@brookes. ac.uk
Fabio Cuzzolin: [email protected]
Applications are invited for a data scientist to support a new, multi-institutional scientific program studying the flow of cerebrospinal fluid in the brain, with the hypothesis that flow is ultimately controlled by neural circuits, often via blood flow modulations. The data scientist will work as part of a Data Science Core, led by Douglas H. Kelley, Mujdat Cetin, and Jiebo Luo, all in the Hajim School of Engineering and Applied Sciences at the University of Rochester. Also employing two PhD students and part-time undergraduate researchers, the Data Core will provide integrated data storage and analysis infrastructure for the four scientific Projects and the Viral Core that together comprise the scientific program.
The Data Core will also build and refine novel data analysis tools and facilitate public sharing of data and software. Diverse and large data will be produced by the Projects, including MRI imaging in humans, two-photon imaging in mice, brain-wide microscopy in mice, electrophysiological measurements in mice, vital sign measurements, computational fluid dynamics simulations, and network simulations of flow and transport through the brain. Key tasks for the data scientist include streamlining existing workflows for data pre-/post-processing and analysis, providing organized and searchable storage infrastructure, developing new statistical and machine-learning algorithms for quantitative information extraction and analysis from multi-modal brain imaging, and linking brain states to CSF flow patterns via artificial intelligence. An MS or PhD in a data-intensive field is required. Experience with imaging, large data sets, and modern ML methods is key; experience with neuroscience or other biological applications is desirable but not required.
The data scientist will enjoy excellent resources and opportunities for collaboration. The position will be affiliated with the Goergen Institute for Data Science (GIDS, directed by Mujdat Cetin) and will involve frequent interactions with the Center for Integrated Research Computing (CIRC), making extensive use of its BlueHive computing cluster. The data scientist will benefit from close collaborative links with the four Projects, led by Maiken Nedergaard (University of Rochester), Patrick Drew (Penn State University), Laura Lewis (Boston University), and Douglas H. Kelley.
Interactions with PhD students and postdoctoral researchers in the Data Core and the Projects will be direct and frequent. Optionally, the data scientist might take on a research faculty role allowing formal mentoring of PhD students. We anticipate hiring for one year, with opportunity to renew; funds are available for five years. Longer-term career opportunities may also be available (e.g., at GIDS or CIRC).
More information is available at www.me.rochester.edu/projects/dhkelley-lab.
The position will be available starting June 1, 2022. For full consideration, applicants should email a curriculum vitae and cover letter to [email protected] by May 1, 2022, however applications will be considered until the position is filled.
We are looking for a candidate for a thesis, whose topic is "Machine learning based approaches for multi-omics data in personalized treatment of sepsis".
The deadline for applications is 30 April.
Laboratoire DAVID : Prof. Karine ZEITOUNI, [email protected], Directrice; Dr. Zaineb CHELLY DAGDIA, [email protected], co-encadrante.
● Prof. Henri-Jean Garchon, [email protected], Prof. Stanislas Grassin Delyle, [email protected], co-encadrants.
Research Engineer / Post-doc position: Explainability and interpretability in Deep Learning
Location: LIG Lab, University Grenoble Alpes (UGA), Grenoble, France
Duration: 18 months (possibly extendable),
Starting Date: Sep/2022
Keywords: Artificial Intelligence, Machine Learning, Deep learning.
Contact: To apply, send a CV and a motivation letter to:
Ahlame Douzal as [email protected]
Context: Explainability in machine learning is part of XAI (eXplainable Artificial Intelligence) field, which has attracted increasing interest in recent years, and whose aim is to make the latent decisions made by an artificial intelligence algorithm humanly explainable and interpretable.
In this work, we focus on the problem of explicabilitý of deep neural models, motivated by two major reasons. On the one hand, deep models that are known to be among the most powerful in artificial intelligence are essential to analyze massive and complex data; particularly encountered in challenging applications.
On the other hand, despite their increasingly impressive performance, deep models are "black box" approaches, whose latent decisions remain obscure and difficult to explain. The objective of this work is, in a first part, to design new approaches aiming at rendering deep neural models explainable and interpretable.
In the second part, the approaches designed will be deployed on use cases from industry and at the heart of societal challenges in the fields of health, energy or the environment. The role of the candidate will be to:
- Collaborate with the researchers of the integrated team to develop methodological and architectural solutions to overcome scientific and technological barriers.
- Participate in the development, test and the deployment of the solution on industrial partners' use cases.
- Participate in the production of the deliverables and the related documentation.
Required profile: - The candidate must have a very good knowledge in machine learning, in deep learning and related architectures (RNN, LSTM, GAN, etc.); having a thesis is a plus.
- Strong programming skills (Java, Python) are required.
- Experience in the development of Deep models is an important asset.
You will find two job offers in Dijon for:
- A computer development engineer (TAL/Apprenticeship) master's level;
- A PhD in computer science specializing in NLP and/or Deep Learning.
https://filesender.renater.fr/?s=download&token=9a129e22-0421-42aa-9aef-cb8887666802
Thank you for disseminating these offers.
thèse/Thesis title : Supervised-unsupervised deep learning using dense associative memories and classical deep learning processes
Laboratoire d’accueil / Host Laboratory : FEMTO-ST. DISC Dept.
Spécialité du doctorat préparé/Speciality : Computer Science (Informatics)
Mots-clefs / Keywords : Deep Learning. Unsupervised. DAM. Enery function.
Stable state
The PhD applicant must have good skills in computer science especially deep learning architectures, machine learning frameworks, high-level programming languages, as well as in applied mathematics particularly numerical approximation methods.
Preferred selection criteria: - Very good knowledge of deep learning - Very good-level programmer - Good skills in numerical algorithms
Personal characteristics: - Methodical - Calm
Financement : MESRI Etablissement
Début du contrat : October, 1, 2022
Salaire mensuel brut : 1975€
Direction de la thèse:/ Thesis Supervisor: Prof. Jacques Bahi BAHI Jacques / [email protected]
Encadrement de la thèse : co-directeur(s) et co-encadrant(s) Co-encadrant : Christophe Guyeux. Professeur Applicants are invited to submit their application to the PhD supervisors.
Application must contain the following documents: - CV - Cover letter - At least 1 reference letter
We are looking for a candidate for a PhD thesis in computer science, the subject of which is "Human/Machine collaboration for complex tasks realization".
Host laboratory: Distributed Knowledge and Artificial Intelligence (CIAD) – http://www.ciad-lab.fr
Belfort, FRANCE
A detailed description of the subject is in the attached PDF, and also available online using this link:
https://www.utbm.fr/wp-content/uploads/2022/04/CIAD-1.pdf
The application deadline is May 20.
Doctoral thesis entitled "ADAPTATIVIT OF THE RESPONSE OF A PLACE OF LIFE CONNECTED TO THE INTERACTIONAL AND BEHAVIORAL CONTEXT OF ITS OCCUPANTS: DESIGN OF AN INTELLIGENT TECHNOLOGICAL INFRASTRUCTURE".
Keywords: sensors, artificial intelligence, smart home, Human Interaction Machine, contextual adaptation Funding: Orange (CIFRE Scholarship)
Contact: Eric Campo, University Professor (LAAS), [email protected] Frédéric Vella, Research Fellow (IRIT), [email protected]
Thesis subject : Understand urban mobility patterns through deep spa- tiotemporal community detection
Keywords Urban mobility; graph dynamics ; community detection ; deep learning; population density ;
multi-source data Candidate profile MSc or equivalent in computer engineering, with strong skills in deep learning (CNNs, RNNs), graph theory, applied mathematics and computer programming (Python, C++, Java).
Advisor Carmen Gervet [email protected] Co-advisor Benjamin Pillot [email protected]
Problem definition The problem under study is the mobility of species at the regional scale within a speci- fied and bounded geographical territory.
More specifically the objective is to extract, represent and analyze the movements and travels of individuals within urban areas through time.
Our main goals are to design and imple- ment methods 1) to identify spatiotemporal human mobility patterns in a city and 2) to study multidimensional factors (social, economic, etc.) that may affect those patterns.
The research lab ESPACE-DEV proposes a PhD topic in the field of AI and urban mobility with keywords being graph dynamics, community detection, multi-source data (See attached file for full description)
ESPACE-DEV (espace-dev.fr) is a multi-disciplinary research unit composed of methodological groups dealing with data science and complex systems modeling, and thematic research studies on socio-ecological systems dynamic and transitions in the realm of climate change.
It is a dynamic unit composed of 90 researchers and academics, 30 engineers, and 59 PhD students. It is located mainly in Montpellier Agropolis (50%) and in the ROMCOM territories (La Réunion, French Guyana, New Caledonia) with strong research sites in Brazil, Madagascar, West and Central Africa.
The PhD candidate will join the data science and modeling research groups and be exposed to the thematic group on energy transition and socio-ecological systems.
Candidate should apply through the ADUM webpage of the doctoral school I2S and can contact Benjamin pillot (benjamin.pillot -- at--ird.fr)