Applications are invited for a full-time post-doctoral research position in the Supaero Reinforcement Learning Initiative, at ISAE-SUPAERO, Toulouse, France. Funding is secured for a 3 year position on a project which aims at reliability in sim2real transfer in RL.

The project is specifically oriented towards the following topics. We are interested in designing new methods for domain adaptation/generalization in RL, to enable efficient transfer to unseen states or new MDPs. Recent work in the team on representation learning for generalization and transfer can be further developed. Robust MDPs and robust RL are also on the agenda. Finally, the project also aims at contributions to data-frugal RL. Besides these key topics, the research agenda is quite flexible and open; both theoretical and applied work is of interest. Preferred applications concern mobile robotics but extensions to other applications are possible.

Keywords: sim2real, domain adaptation/generalization, transfer, robust RL, data frugality, representation learning for generalization.

The Supaero Reinforcement Leaning Initiative (SuReLI) is a group of professors, researchers, engineers, students, researching open questions in Reinforcement Learning. We actively promote an incusive, friendly work atmosphere. Although we participe in the current race for research in ML, we are also outspoken promoters of the virtues of slow science. We are located in ISAE-SUPAERO, the world leading institution in aerospace engineering higher education, in the lively city of Toulouse, regularly ranked best student city in France. SuReLI’s recent research spans topics such as representation learning for RL [1-3], RL in non-stationary MDPs [4], evolutionary RL and optimization [5,6], distillation of deep RL policies [7], optimization for RL [8], neural architecture search [9], and applications to robotics, fluid flow control, software testing, video games, etc.

Candidates should hold a PhD in a relevant discipline (computer science, mathematics, control theory, etc.), be scientifically curious, technically autonomous and able to suggest original research directions. Applicants should hold a good publication record at top-tier ML conferences. Group involvement and student advising within SuReLI is encouraged, so applicants should have demonstrated their advising or leadership capabilities in previous projects.

Candidates are invited to contact Emmanuel Rachelson ([email protected]) to discuss their research project and for further details on the position and the application process.

Key words: machine learning on time series, clustering, classification, average time series

Context Surgical robotics is now widely used with, for instance, more than 5000 Da Vinci systems and one million procedures performed worldwide. Surgery is a complex activity, in a very small anatomical volume, and with a lot of variability between patients and between surgeons. The global objective of the two-year SPARS (Sequential Pattern Analysis in Robotic Surgery: Understanding Surgery) project led by the MediCIS team (LTSI (1), Inserm, Rennes 1 University) is to develop data analysis approaches being able to provide a better understanding of the surgical practice, from complex surgical data. The approaches will be developed thanks to the complementary skills available in the project’s consortium, including time series analysis. In this consortium, the IRISA laboratory (Rennes and Vannes) is calling for applications for a post-doctoral research position (duration two years) on time series analysis.

(1) Laboratory Signal and Image Processing

Missions In the SPARS project context, a trajectory compiles information on the 3D location of the tip of a surgical instrument at the hands of the surgeon, at a constant frequency. The candidate will be mostly involved in one of the three workpackages of the SPARS project. A first task will focus on clustering and classification for such trajectories. Various practical objectives are pursued, including the generation of a model corresponding to a cluster or a class, the characterization of operating modes specific to a type of patient or a type of surgeon, the provision of advice to practitioners in the case of robotic surgeries that are not or not very well documented, the identification of the level of expertise of a practitioner, the prediction of the surgical procedure to be chosen according to the type of patient. These investigations will use dissimilarity measures based on temporal alignment, as DTW [SC71] or elastic kernels as proposed in [CVB07], [CB17] and [M19a]. This task will also address co-clustering for trajectories. The investigations will focus on how to combine time series with other types of data for a co-clustering purpose, using either deep learning [XCZ19] if enough data is available, symbolic representation [BBC15] or latent block [BLN20] models that all need to be adapted to the specificity of kinematics data.

Once a cluster or a class is obtained, another task will be to compute an average trajectory from a set of trajectories. The practical objectives will be the following: highlight deviations from the average trajectory that are potentially interpretable (as characteristics of the practitioner, or of the patient, for example) ; identify the best operating mode to young practitioners or trainees if it is possible to correlate the operating mode with clinical results. Intuitively, on the graphical representation of a time series, variability related to temporality (phase) concerns the abscissa axis, and variability related to shape concerns the ordinate axis. To compute a consensus trajectory, the second task of the package will examine how to extract the atemporal form and the variable component related to temporality, assuming that this atemporal form may be interpreted as an approximation of the consensus. The problem of shape and phase separation has been studied in [PZ16], [SSV10] and [M19a]. The second task will examine how to improve the preliminary work in [M19b], notably by proposing other kernels.

Requirements for this position Doctorate in computer science, applied mathematics and computer science, or mathematics, with a specialization in machine learning

Master's degree in computer science, applied mathematics and computer science, data science, or mathematics with a specialization in machine learning. - strong knowledge and solid experience in temporal data analysis,

- mastery of data manipulation, relying on machine learning libraries,

- programming experience, good programming skills (notably in Python) and technical ability to manage a code development project,

- ability to work in a team, and report on the progress of work.

Some knowledge in deep learning will be a plus.

The personal qualities expected are mostly autonomy and interest in interdisciplinarity (health), as well as writing skills (both in French and English). Fluency in French will be a plus.

Work environment Location: Institute Research in Computer Science and Random Systems (IRISA), University of Rennes 1 - Beaulieu Campus, 263 Av. Général Leclerc, 35000 Rennes

Duration: 24 months – Applications will be accepted until the position is filled (for recruitment by 1 December 2022 at the latest)

Host team: LINKMEDIA

The successful candidate will work with four academic researchers from IRISA / Rennes / LINKMEDIA team (Simon Malinowski, Associate Professor in Computer Science), IRISA / Vannes / EXPRESSION team (Pierre-François Marteau, Full Professor in Computer Science), LS2N (2) / Nantes / DUKe team (Christine Sinoquet, Associate Professor with French Accreditation to supervise Research (HdR)) and INSERM / Rennes / LTSI MediCIS team (Pierre Jannin, Directeur de recherche INSERM, HdR). The successful candidate will collaborate with the partners in the project, among which the other post-doctoral fellow involved in the project and the project partners experts in surgery and in surgical data analysis.

(2) Digital Sciences Laboratory of Nantes: UMR CNRS 6004

Income: 2160,26 euros before taxes monthly

How to apply? Documents to be provided :

Profile with Doctorate: - detailed Curriculum Vitae including a complete list of publications - letter of motivation indicating the candidate’s research interests and achievements to date,

- a selection of publications,

- letters of recommendation for the current year,

- contact details of two referees (at least) with whom the candidate has worked (first name, surname, status, institution (give details of acronyms if applicable), city, e-mail address, telephone number)

Profile with Master degree: - detailed Curriculum Vitae - letter of motivation,

- Master 2 marks (with rank and number of students in the year) - letters of recommendation for the current year,

- contact details of two referees (at least) with whom the candidate has worked (first name, surname, status, institution (give details of acronyms if applicable), city, e-mail address, telephone number)

Questions or application files (zip archive only) should be sent to the four contact persons below:

[email protected]

[email protected]

[email protected]

[email protected] (SPARS project leader)

Simon Malinowksi http://people.irisa.fr/Simon. Malinowski/

Christine Sinoquet https://christinesinoquet. wixsite.com/christinesinoquet

Pierre-François Marteau https://people.irisa.fr/ Pierre-Francois.Marteau/

Pierre Jannin https://medicis.univ-rennes1. en/members/pierre.jannin/index

Applications are invited for a Postdoctoral research position in the cross-cutting fields of safety and qualification of trusted autonomous systems at the CEA LIST.

We seek applicants who ideally have background in one or more of the following areas: safety processes and analysis methods, AI algorithms foundations, model-driven engineering (MDE).

The successful candidate will join the LSEA (Embedded and Autonomous Systems Design Laboratory). The LSEA carries out research on methods, design principles and tools for the engineering of efficient and trustworthy embedded and autonomous systems.

The lab conducts high-impact research in close collaboration with industry, combining proven systems and software engineering best practices with applied artificial intelligence to provide effective and scalable advanced technologies of safe self-adaptation and integration of trustworthy autonomy in critical systems.

To apply, please send a CV including background information, detailed work experience, list of publications to [email protected] and [email protected].

The LaBRI research lab based at the University of Bordeaux is currently seeking highly motivated candidates with excellent academic records and experience in knowledge representation and reasoning (esp. ontologies, description logics, inconsistency and uncertainty handling), database theory, data quality and/or logic in CS to join the Ratio team (ratio.labri.fr/) and take part in the INTENDED AI Chair (intended.labri.fr).

The postdoctoral researcher will engage in foundational research related to the project's overall theme of developing principled methods for handling imperfect (inconsistent, inconsistent, and/or uncertain) data, by exploiting declarative knowledge and reasoning.

Generally speaking, this will involve the definition of formal frameworks for repairing and querying imperfect data, the study of the computational complexity of the associated reasoning tasks, and the development of reasoning algorithms.

However, the precise research topic will take into account the background and interests of the postdoctoral researcher, so brilliant candidates with an interest for the overall project topic should not hesitate to apply!

A more detailed description of the position, topic, research environment, desired profile, and application procedure can be found here:

http://intended.labri.fr/documents/postdoc-intended.pdf

Applications will be reviewed on a regular basis until a suitable candidate is found.

The desired starting date is February-April 2023, and should be no later than September 2023.

An 18-month engineering position is to be filled at the LASTIG laboratory of IGN, Gustave Eiffel University from January 2023 as part of a European project, SUBDENSE.

The job description is available online: https://www.umr-lastig.fr/ lastig_data/pdf/OpenPosition-DashboardEngineer-LASTIG.pdf

The position will be located at IGN-ENSG, Saint Mandé (bordering Paris).

CONTEXT: The SUBDENSE project seeks to better understand the phenomenon of suburban densification. It is an important subject in tackling climate change by reducing net land take and suburban areas within our cities have the greatest potential for densification. Data science and spatial analysis is combined in this project with socio-anthropological approaches (Cultural Theory) and spatial planning across different institutional contexts to identify the conditions for a successful densification.

The project consists of a consortium of four research partners in Germany (Technical University of Dortmund, and Leibniz Institute for Urban and Rural Ecology), France (LASTIG laboratory) and the UK (University of Liverpool). The candidate will work as part of the French team of the project, at LASTIG. There will be opportunities to engage with all partners of SUBDENSE.

A post-doctoral researcher position is available within the GRETTIA laboratory working on statistical learning for the processing of urban big data.

It is part of a collaborative project funded by the French National Research Agency (ANR MobiTic Project, https://mobitic.huma-num.fr/).

Several partners are involved in the project: two laboratories from Gustave Eiffel University (Grettia-Coordinator and Licit) but also the SENSE laboratory of Orange Labs, the SSP lab of INSEE.

This project aims to produce relevant, reliable, representative and frequently updated indicators of the presence and mobility of people, by combining massive digital data (telephone signalling data, tele-ticketing data, etc.) and traditional data (censuses, socio-demographic data from INSEE,...).

More details are available in the attached job description.

To apply, send a CV and a cover letter to the three addresses below:

Latifa Oukhellou : latifa.oukhellou@univ-eiffel. .fr

Etienne Côme: [email protected]

Angelo Furno : [email protected]

Place of work: GRETTIA Laboratory, Gustave Eiffel University, Champs-sur-Marne.

IMT Atlantique/Lab-STICC/INRIA team Odyssey has several open 2-year post-doc positions on deep learning and computational imaging for ocean data assimilation and forecasting, more specifically on:

- Deep emulators for marine plankton dynamics: - Deep learning and DA for ocean forecasting and reanalyses:

https://cia-oceanix.github.io/downloads/postdoc_offer_anr_dream2022. pdf

https://cia-oceanix.github.io/ downloads/postdoc_offer_edito_model_lab2022.pdf

- Learning-based reconstruction of upper ocean dynamics from underwater acoustics data:

https://cia-oceanix.github.io/ downloads/postdoc_OceaniXShom2022.pdf

These postdocs will explore how deep learning schemes provide new means to revisit and advance inverse problems in ocean modeling and monitoring. More details in the above links and https://cia-oceanix. github.io. Targeted contributions cover demonstrations from idealized/simulated systems to real satellite/in situ observation datasets. We welcome both applicants with a background in applied math./geoscience and interest in deep learning as well as applicants with a background in data science and interest in ocean science.

Deadline for applications: the review of applications will begin immediately and continue until the positions are filled.

The ESTAS (Evaluation and Safety of Automated Transport Systems) lab is opening a Master thesis on the topic of "bridging discrete and continuous neural network models".

The work planned in this Master thesis takes its main motivation in the use of artificial intelligence modules in perception tasks of autonomous vehicles (detection and recognition of road signs, rail signals, or other elements of the vehicle’s environment).

Most of these perception modules currently rely on neural networks, which take the form of a discrete graph with a finite sequence of layers, each containing a finite number of neurons where mathematical operations (linear transformations and non-linear activation functions) are applied.

However, a new class of model called neural ODE (neural ordinary differential equation) has recently been introduced, where the differential equation can be seen as a continuous generalization of a neural network.

The main objectives of this Master thesis are the following:

Literature review of existing neural ODE models and their relation to discrete neural networks.

Designing new neural ODE models based on the continuous generalization (of the time, depth, width, ...) of a neural network.

Establishing formal relations between the discrete and continuous neural models, and using them to deduce the safety properties of one model based on the safety verification of the other.

Comparisons, on image recognition benchmarks, of the performances of the discrete and continuous neural models, in terms of both training and safety verification.

More details on desired profile, application procedure, and related open PhD position are provided in the following link:

https://gdr-macs.cnrs.fr/sites/default/files/2022-11/2023_Master_thesis_neural_ODE.pdf

I am looking for a postdoc on the ANR TRANS3 project (following the eFran Fluence).

The topic is about the automatic evaluation of the fluency of young readers. For more information and to apply:

https://emploi.univ-grenoble-alpes.fr/offres/chercheur-researcher-post-doctoral-evaluation-automatic-of-fluence-for-learning-of-reading-1164624.kjsp? HR=1135797159702996

Canterbury University invites applications for a *fully funded PhD position* at the School of Computing (ETD).

Title: Developing a machine learning predictive model and system for infectious disease.

Short summary: A major element of personalized medicine involves the identification of therapeutic regimes that are safe and effective for specific individuals. It is a data-driven approach that relies on artificial intelligence and (big) multi-modal data from an individual to make patient-tailored decisions.

The recent successes in using Artificial Intelligence/Machine Learning (AI/ML) across a wide range of difficult problems in various disciplines have inspired research in applying ML techniques to infectious disease prediction. Recent research in artificial intelligence models have reported some success in predicting some medical conditions such as heart disease, cancer and diabetes. The use of AI/ML in the field of infection management has been reported as being at its infancy. Currently, treatment decisions are not informed by data-driven models of patient risk for complications. Some existing machine learning clinical decision support systems including the ones developed for infection prediction still face a great number of challenges such as data quantity (including the number and the nature of features considered), data quality, model interpretability, evaluation in real world settings, deployability and sustainability.

The objective of this PhD project is to investigate further whether infection caused by opportunistic pathogens can be predicted using machine learning. It also evaluates the impact of non-clinical data (socio-economic data/genomic data) on the machine learning predictive model.

Research questions: • How can machine learning improve the prediction of infection occurrence?

• What is the impact of non-clinical data (and features) on the predictive AI/ML model?

• How can the results of AI/ML predictions be interpreted according to clinicians needs.

Your experience and ambitions: We welcome candidates with a master’s degree (or international equivalent) in computer science, health informatics, or any relevant STEM field, who are curious about applied machine learning in biomedical sciences. Work experience may also be considered. We seek colleagues who enjoy coding, scripting and analytics, and who are keen to push the boundaries of machine learning and artificial intelligence. This project requires creative thinking and programming. Prior machine learning experience is a merit but not a requirement. We further appreciate willingness to travel, collaborate and communicate science.

Ready to apply? For further information about this project, to apply for the position, please submit your application including the attachments mentioned below as one single PDF document in English to: Dr Scott Turner: [email protected], Dr Leishi Zhang: [email protected], and Dr. Amina Souag: [email protected].

(1) Letter of motivation.

(2) CV (including list of publications, if any).

(3) Contact details of at least two referees (or letters of recommendation, if already available).

The position will be filled as soon as a suitable candidate is identified.

About CCCU – Canterbury: The research of this project will be undertaken within School of Engineering, Technology, and Design (ETD) at Canterbury Christ Church University (CCCU). The project will be developed in collaboration with academics from Natural and Applied Sciences section at CCCU and East Kent Hospitals University NHS Foundation Trust.

Canterbury Christ Church University is located in the world-famous Cathedral city amongst stunning history and heritage. Canterbury is a thriving international destination, with many students and staff choosing to study and work here, making this historic, cosmopolitan city vibrant and culturally diverse. We are strongly committed to equality and recognise the value of diverse students and staff.

Manager - employment rate to be agreed (between 50% and 100%) for a fixed period of 12 months (with possibility of extension) - for the Language and Communication Institute (ILC), of the Human Sciences Sector (SSH)

- in Louvain-la-Neuve - Starting date: early 2023

Background - Current research makes extensive use of written and oral language data, in different languages (French, Spanish, English, Dutch, etc.).

To be usable, this language data must be documented (metadata), anonymized (in order to comply with the rules on personal data ), enriched with annotations (transcription, indexing, thematic analysis, etc.) and deposited in searchable online databases.

It is to these different tasks that the data manager will contribute within the Language and Communication Institute (ILC), and more particularly the Linguistic Research Centre (PLIN) and the CENTAL platform (Centre for Natural Language Processing).

Function - In collaboration with PLIN/ILC researchers, the functions of the data manager are to: - Supervise the processing chain of oral and written corpora (data acquisition, metadata documentation, transcriptions and annotations, transfer to databases)

existing, standardization of the formats used)

- Develop data preprocessing and processing tools (segmentation, alignment of text to sound, text-to-text alignment, automatic or semi-automatic annotation, etc.)

- Ensure a technological watch for the interoperability of data (documented and processed according to international standards , cf. Clarin, Ortolang, Olac, etc.) and the improvement of data acquisition (automatic speech recognition, tokenization, etc.)

- Ensure compliance with legal and ethical conventions related to data protection (e.g. GDPR) and data publication (e.g. Dataverse)

- Represent UCLouvain in various international data consortia in linguistics.

- Follow up on requests for information and support made to our future K CLARIN center on learner corpora

Qualifications and skills required - The candidate will meet the following qualifications: - holder of a Master's degree in Language Sciences, Natural Language Processing or Linguistics

- programming skills:

Perl and/or Python, good knowledge of XML

- ability to process language data in at least 2 of these languages (French, English, Dutch, Spanish, German, etc.)

- knowledge of English (B2) and in particular academic English (to participate in international meetings and contribute to research publications)

- sense of teamwork, ability to listen and analyze needs, responsiveness

- notions in linguistic statistics are a plus

Your application (single file with letter of application, curriculum vitae) must be sent by 10 January to the following address: [email protected]

On the basis of these documents, candidates will, if necessary, be selected for an interview to be held during the second half of January.