For a project run jointly by Friedrich-Alexander-Universität Erlangen-Nürnberg and the University of Oxford we are seeking a

POSTDOCTORAL RESEACHER IN COMPUTER VISION / MACHINE LEARNING (100%, E13 TV-L, starting asap)

In our project "World Futures: Multimodal Viewpoint Construction by Russian International Media", we study how words, gesture and prosody combine in meaning making. We need your help with the following tasks:

- Improve gesture detection and gesture recognition on our datasets based on hand-annotated data, automatically-annotated data (e.g. with OpenPose), and previous work done by collaborating groups

- Develop a model to identify viewpoint manipulation based not only on visual but also on textual and auditory cues (towards the end of the project runtime; with support from experts in the field and based on manual annotation created during the project).

While the post is officially located in Erlangen, support, training and supervision will be shared by FAU (Peter Uhrig in collaboration with the Pattern Recognition Lab) and Oxford (Phil Torr and his team of the Torr Vision Group).

FAU is a family-friendly employer and intends to increase the number of women in research and teaching positions and, therefore, strongly encourages female researchers to apply. In its pursuit of academic excellence, FAU is committed to equality of opportunity and to a proactive and inclusive approach, which supports and encourages all under-represented groups, promotes an inclusive culture and values diversity.

The position is full-time and is paid at the salary level E13 TV-L. The contract runs until 31 January 2024.

Please apply by submitting a cover letter and your CV to Dr. Peter Uhrig ([email protected]) at your earliest convenience. The position remains open until filled.

Dear Colleagues, Please consider this two-year post-doc position in Saint-Étienne, France, on Machine Learning and Bioacoustics.

https://laboratoirehubertcurien.univ-st-etienne.fr/en/teams/data-intelligence/job-opportunities/post-doctoral-position-2-years-on-artificial-intelligence-and-bioacoustics.html

Rémi Emonet Laboratoire Hubert Curien, UMR CNRS 5516, Université Jean Monnet de Saint-Etienne, Univ. Lyon

Faculty of Technology Design and Environment School of Engineering, Computing and Mathematics

PhD Studentship: 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: March 10 2022

Start date: June 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 June 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.

Project description 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.

https://www.epistemic-ai.eu/

https://www.youtube.com/watch? v=GNCKqoQODR0&t=3s

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.

Requirements 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.

How to apply

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 via https://sites.google.com/brookes.ac.uk/tde-research/studentships-how-to-apply? authuser=0 - 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 10 2022.

Enquiries: Dominic Maitland: [email protected]

Dear colleagues, We are looking for an enthusiastic and proactive Ph.D. student to join the Aerial Robotics Research Facility (https://knurobot.wixsite.com/arrf) at Kyungpook National University (KNU) in South Korea. We’d love to hear from applicants for the following domains.

- Aerial Robotics, Drones, Unmanned Aerial Vehicles (UAVs), Urban Air Mobility (UAM) - Autonomous Flight, Autonomous Navigation - Autonomous Cooperation of Multiple Heterogeneous Unmanned Vehicles

- Computer Vision, Robot Vision, Perception - Simultaneous Localization and Mapping (SLAM) - Visual-Inertial Fusion - Visual Servoing

To apply, send your resume / CV to [email protected]. Be sure to include Robotics PhD Student and your name in the subject line. If you have any questions regarding this post, please feel free to contact Prof. Kyuman Lee [email protected].

Best regards, * Aerial Robotics Research Facility (email to Prof. Kyuman Lee: [email protected]; website: https://knurobot. wixsite.com/arrf)

Kyuman Lee, Ph.D. Assistant Professor Director, Aerial Robotics Research Facility @ KNU Dept. of Robot and Smart System Engineering Kyungpook National University

Phone: +82-53-950-4570

Email: [email protected]

Web: https://knurobot.wixsite. com/arrf

The Artificial Intelligence in Health and Nutrition (AIHN) laboratory of the ARTORG Center invites researchers with strong background in machine learning to apply for a newly opened postdoctoral position in the fields of treatment optimization and disease management.

We are looking for enthusiastic, motivated, and skilled candidates to join our research group. The candidates need to have strong background and experience in machine learning, particularly in deep and reinforcement learning, and show outstanding commitment to multidisciplinary research.

The candidates must have already obtained a doctorate (PhD), no more than 2 years ago, in machine learning from a well-recognized university and have a strong publication record with at least one publication at IEEE TMI, IEEE MM, IEEE JBHI, MICCAI, ECCV, CVPR, ICML or equivalent conferences and journals.

Proficient oral and written English skills are expected. The researcher will be part of the AIHN laboratory and will work with a team of engineers, computer scientists, and healthcare professionals on the introduction, usage, and validation of highly innovative machine learning approaches for healthcare.

General Information about the Position The position is funded for a period of 2-years at first with the possibility to be prolonged for up to 4 years. Salary will be according to Swiss University regulations. The start date is expected to be in June 2022.

About the University of Bern The University of Bern is located at the heart of Switzerland. Internationally connected and regionally anchored, it cultivates exchange with society and strengthens partnerships between science, medicine, business, and politics. The University of Bern is committed to a deliberate and ethical responsibility towards people, animate and inanimate nature.

As an important educator, promoting enterprise and industry in the region and beyond, it distinguishes itself through problem-oriented research into questions of pressing social relevance. The University of Bern is an equal opportunity employer, promotes healthy work-life-balance and safe working environments, and strives to increase the number of women at all levels in its faculties.

Application If you are interested in working in a group with an international, interdisciplinary profile please send the application to Prof. Dr. Stavroula Mougiakakou ([email protected]). Applications should include: i) research statement, ii) detailed curriculum vitae, iii) list of publications, iv) contact details of three referees. The documents should be combined into ONE pdf file.

Applications will be reviewed until the position has been filled.

The ARTORG Center for Biomedical Engineering Research is the University of Bern ́s transdisciplinary Center of Excellence for medical technology research.

Its mission is to tackle unmet clinical needs and envision future challenges in diagnosis, monitoring and treatment to create viable healthcare technology solutions with imagination, agility and purpose.

Its projects run from discovery and basic research to clinical translation. The Artificial Intelligence in Health and Nutrition (AIHN) laboratory of the ARTORG Center has opened two

(2) Ph.D. student positions in machine learning. The duties of the successful candidate include interdisciplinary collaboration and research in the following topics

• Machine learning with emphasis on deep learning and/or reinforcement learning algorithms • Computer vision and image processing • Data-driven prediction models aiming at novel methods for applications in healthcare and nutrition.

Requirements • Experience and/or course work in machine learning (Essential). • MSc in computer science, engineering, or applied mathematics. • Solid programming skills in Python or C/C++/ C#. • Excellent command of English. • Commitment to multidisciplinary research.

General information The candidates will be affiliated with the University of Bern as PhD students. The positions are funded for a period up to 4 years. Salary will be according to Swiss University regulations. The lab offers an international environment and the possibility to conduct excellent research.

The PhD projects are part of a larger consortium and requires close collaboration with engineers, computer scientists, healthcare professionals, as well as with industrial partners.

Application If you are interested in working in a group with an international, interdisciplinary profile please send the application to Prof. Stavroula Mougiakakou ([email protected]). Applications should include: i) motivation letter, ii) detailed curriculum vitae, iii) abstract of the master thesis, iv) contact details of two referees. The documents should be combined into ONE pdf file.

The start date is expected to be in June 2022. Applications will be reviewed until the position has been filled. About the University of Bern The University of Bern is located at the heart of Switzerland. Internationally connected and regionally anchored, it cultivates exchange with society and strengthens partnerships between science, medicine, business, and politics. The University of Bern is committed to a deliberate and ethical responsibility towards people, animate and inanimate nature.

As an important educator, promoting enterprise and industry in the region and beyond, it distinguishes itself through problem-oriented research into questions of pressing social relevance. The University of Bern is an equal opportunity employer, promotes healthy work-life-balance and safe working environments, and strives to increase the number of women at all levels in its faculties.

POSTDOC POSITION: 45,000 students and 8,000 employees in teaching, research and administration, all working together to shape perspectives for the future – that is the University of Münster (WWU). Embedded in the vibrant atmosphere of Münster with its high standard of living, the University’s diverse research profile and attractive study programmes draw students and researchers throughout Germany and from around the world.

The Computer Science Institute in the Faculty of Mathematics and Computer Science at the University of Münster (WWU), Germany, is seeking to fill the position at the earliest possible date of a

Postdoctoral Research Associate Wissenschaftliche*r Mitarbeiter*in (salary level TV-L E 13) in the externally funded project "GIGA Sign Language" (5G.NRW funding scheme). A full-time position (PostDoc) is offered, limited until 31 December 2023. The aim of this project is to develop a machine learning-based app for translating sign language from images to text and vice versa. For this purpose, various methods for hand gesture recognition, face recognition and other methods originating from the field of image analysis will be investigated. This project is being carried out in cooperation with partners from research and industry. The overall goal is the development of a smart phone app that sends high-resolution video material to a central AI server via 5G and thus enables communication between deaf and non- deaf people.

Your tasks • Collaboration in the third-party funded project "GIGA Sign Language" (5G.NRW)

• Design, implementation and training of relevant deep learning methods such as Convolutional Neural Networks, Recurrent Neural Networks and Transformers • Development of computer vision techniques (e.g. for pre-processing video material)

• Development of novel end-to-end machine learning systems • Collaboration with partners from research and industry

Our expectations • A scientific university degree (Diplom/Master) as well as a completed doctorate in computer science, mathematics or a similar scientific field of study are required.

• Demonstrated experience in machine image analysis and machine learning • sound theoretical knowledge in the field of neural network training

• Experience in training and analysis of Deep Neural Networks for image analysis • Initial experience with machine learning based translation algorithms, Natural Language Processing or Optical Character Recognition.

• A good command of written and spoken English is an advantageous. • Furthermore,softskillssuchastheabilitytoworkinateam,communication skills, organizational skills and independent working methods are required

Additionally desirable are: • § Experience with PyTorch and Tensorflow. • § Hands-on experience in the required programming tools (e.g., TensorFlow, Pytorch)

• Demonstrated knowledge of the associated analytical methods

The University of Münster is an equal opportunity employer and is committed to increasing the proportion of women academics. Consequently, we actively encourage applications by women. Female candidates with equivalent qualifications and academic achievements will be preferentially considered within the framework of the legal possibilities.

The University of Münster is committed to employing more staff with disabilities. Candidates with recognised severe disabilities who have equivalent qualifications are given preference in hiring decisions.

Positions can generally be filled as part-time positions if there are no compelling work-related reasons against doing so.

If you have any questions in advance, please contact Prof. Dr. Benjamin Risse (0251 83 32717 / [email protected]).

Are you interested? Then we look forward to receiving your application by 04 March 2022 at:

Westfälische Wilhelms-Universität Institut für Informatik Prof. Dr. Benjamin Risse Einsteinstr. 62 48149 Münster

You may also send us your application electronically in PDF format to [email protected]. Please note that we cannot consider other file formats.

PhD POSITION: 45,000 students and 8,000 employees in teaching, research and administration, all working together to shape perspectives for the future – that is the University of Münster (WWU). Embedded in the vibrant atmosphere of Münster with its high standard of living, the University’s diverse research profile and attractive study programmes draw students and researchers throughout Germany and from around the world.

The Computer Science Institute in the Faculty of Mathematics and Computer Science at the University of Münster (WWU), Germany, is seeking to fill the position at the earliest possible date of a

Research Associate Wissenschaftliche*r Mitarbeiter*in (salary level TV-L E 13)

for the externally funded project "GIGA Sign Language" (5G.NRW funding scheme). We are offering a part-time position (75%) commencing limited until 31 December 2023. The aim of this project is to develop a machine learning-based app for translating sign language from images to text and vice versa. For this purpose, various methods for hand gesture recognition, face recognition and other methods originating from the field of image analysis will be investigated. This project is being carried out in cooperation with partners from research and industry. The overall goal is the development of a smart phone app that sends high-resolution video material to a central AI server via 5G and thus enables communication between deaf and non-deaf people.

Your tasks • Collaboration in the third-party funded project "GIGA Sign Language" (5G.NRW)

• Design, implementation and training of relevant deep learning methods such asConvolutional Neural Networks, Recurrent Neural Networks

• Development of computer vision techniques (e.g. for pre-processing video material)

• Collaboration with partners from research and industry Our expectations

• A scientific university degree (Diplom/Master) in computer science, mathematics or asimilar science is required.

• Demonstrated experience in machine image analysis and machine learning

• sound theoretical knowledge in the field of neural network training

• Experience in training and analysis of Deep Neural Networks for image analysis

• Initial experience with machine learning based translation algorithms, Natural LanguageProcessing or Optical Character Recognition.

• A good command of written and spoken Englishis an advantageous.

• Furthermore,softskillssuchastheabilitytoworkinateam,communication skills,organizational skills and independent working methods are required

Additionally desirable are: • Experience with PyTorch and Tensorflow.

• Hands-on experience in the required programming tools (e.g., TensorFlow, Pytorch)

• Demonstrated knowledge of the associated analytical methods

The University of Münster is an equal opportunity employer and is committed to increasing the proportion of women academics. Consequently, we actively encourage applications by women. Female candidates with equivalent qualifications and academic achievements will be preferentially considered within the framework of the legal possibilities.

The University of Münster is committed to employing more staff with disabilities. Candidates with recognised severe disabilities who have equivalent qualifications are given preference in hiring decisions.

If you have any questions in advance, please contact Prof. Dr. Benjamin Risse (0251 83 32717 / [email protected]).

Are you interested? Then we look forward to receiving your application by 04 March 2022 at:

Westfälische Wilhelms-Universität Institut für Informatik Prof. Dr. Benjamin Risse Einsteinstr. 62 48149 Münster

You may also send us your application electronically in PDF format to [email protected].

Please note that we cannot consider other file formats.

Post-doctoral position Representing and enriching events in knowledge graphs

Keywords: machine learning on graphs, knowledge graph, event prediction

Context: XP-event project (2021-2024)

An event is defined as “anything that happens, anything that fits over time”: meetings, phone calls, purchases, but also business buyouts, change of management, health crises, etc. The events are shared at through various communication channels that can be private (internal documentation, emails, Slack, Teams, phone, etc.) or public (press, Twitter, Facebook, etc.). Knowledge of these events is essential for humans to make decisions which themselves will have an impact on future events. Many innovative applications can benefit or even emerge from a technology capable of extracting events from various sources, representing them, aggregating them and exploiting them to predict future events. We can for example cite: anticipating demand for sanitary products, the supervision of cultural, advertising or festive events, but also the study of competition, the study of commercial markets, etc. .

One of the main obstacles to the deployment of these applications is the excessively high cost of their development when it is carried out on an ad hoc basis by competing players. The XP-Event project proposes to respond to this difficulty by setting up a common base for all the applications organized around the notion of event. This project is led by a consortium naturally formed by two companies (GeoTrend and Emvista) and a research team from the IRIT laboratory sharing this vision and each having significant scientific and technological heritage in the field.

Position Description The candidate will contribute to the tasks in which IRIT is involved, and will be more particularly in charge of realizing and implementing the proposed solutions. The first task concerns the representation of event graphs. The first task will be to define an adapted ontology and a process allowing to exploit it to access or represent in RDF the graphs of the industrial partners of the project, which are graphs of quite different nature. The second task aims at defining a process to evaluate the quality of the event graphs. Evaluation will be based on the ontology structure as well as on reasoning from the knowledge graph.

The third task concerns the enrichment of these graphs. Two types of approaches will be implemented in the project, and for each of them research will be needed to advance the state of the art. The first approach consists in extracting information from texts. Each of the industrial partners already has its own processing chain that it will improve and unify. The second approach consists in exploiting the current state of a graph but also the structure of an event in the ontology to suggest the addition of new nodes or new relations to the graph. This approach will be implemented through learning algorithms from graph.

Requirements for this position Applicants are required to have a PhD in computer science, and strong background ideally in two areas of artificial intelligence: semantic web technologies (ontology engineering, linked data management and querying, SPARQL, SHACL, RuleML, ...), and machine learning from graphs and vector, recursive neural networks, etc. Good programming skills (Python, OWL API) and experience in participating in collaborative projects is required. In addition, the candidate must have a taste for innovation, and the ability to dialogue and collaborate with industrial partners. Experience in managing graph warehouses (Virtuoso, Strabon, Neo4j...) is desired. Fluency in written / spoken English is required too. Fluency in French language will be a plus.

Work environment Location : Institut de Recherche en informatique de Toulouse (IRIT) - UPS, 118 Route de Narbonne F-31062 Toulouse Cedex - France and et UT2J, 5 allées Antonio Machado 31300 Toulouse

Duration: 24 months – starting as soon as possible (at best on May 1st, 2022)

Host team: MELODI https://www.irit.fr/en/departement/dep-artificial-intelligence/melodi-team/

The candidate will work with four academic researchers from MELODI (F. Benamara, Ph. Muller, N. Aussenac-Gilles and N. Hernandez). He will collaborate with the partner companies in the project, namely Geotrend, located in Toulouse, and Emvista, located in Montpellier.

Income: between 2300 and 2800 euros before taxes (brut) monthly according to past experience

How to apply? Applicants should send their application files before March 30th 2022 to the contact persons listed here below. Application files should contain at least a full Curriculum Vitae including a complete list of publications, a cover letter indicating their research interests, achievements to date and vision for the future, as well as either support letters or the name of 2 persons that have worked with them.

Applicants should contact: N. Aussenac-Gilles ([email protected]) and N. Hernandez ([email protected])

1 Post-doc position (2 years) on XAI & Argument Mining in Medicine* *Université Côte d’Azur, CNRS, Inria, I3S, France*

Title: _Integrating machine predictions and argumentative explanations for explanatory argument-based dialogue_

*-- Context --* Argument(ation) mining, the new and rapidly growing area of Natural Language Processing (NLP) and computational models of argument, aims at the automatic recognition of argument structures in large resources of natural language texts. In the clinical domain, argument mining has been proved to be beneficial in providing us with methods to automatically detect in text the argumentative structures that are at the basis of Evidence-Based Medicine (EBM).

While neural networks for medical diagnosis have become exceedingly accurate in many areas, their ability to explain how they achieve their outcome remains problematic. The goal of this project is to enable clinical prediction systems to engage in argument-based explanatory dialogues with humans, whereby clinicians will be able to argue with the system in natural language. In particular, we focus on elaborating argumentative explanations to diagnosis predictions, so that to support clinicians to make informative decisions.

The postdoc positions is funded by the CHIST-ERA ANTIDOTE project.

*-- Keywords --* Evidence-based decision making in medicine, argument-based explanation, explanatory natural language dialogues

*-- Research fields --* Artificial Intelligence, Natural Language Processing, Machine Learning

*-- Research group --* WIMMICS (http://wimmics.inria.fr/) is a research team of Université Côte d’Azur (UCA), Inria, CNRS. The research fields of the team are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities.

*-- Candidate profile --* The candidate must hold a PhD in Computer Science, with a specialization in Artificial Intelligence or Natural Language Processing. The candidate must have strong skills on Machine Learning frameworks and good English skills in writing and communication.

*-- Application process --* Apply by sending an email directly to the supervisors (Elena Cabrio, [email protected] and Serena Villata, [email protected]).

*Deadline for applications: *** March 30th, 2022 ****

The application must include: - Curriculum vitæ. - Motivation Letter. - At least one letter of recommendation.

Postdoc position in DL and XAI for Earth sciences - ESA project

The Image and Signal Processing (ISP) group at the University of València (Spain) is looking for a motivated postdoc in the intersection of machine learning, computer vision and Earth sciences.

The postdoc position is fully funded by the European Space Agency (ESA) project "DeepExtremes -- AI4Science: Multi-Hazards, Compounds and Cascade events".

Brief description ----------------------- Climate extremes are on the rise. This is one of the most critical manifestations of climate change as extreme events have multiple impacts: from declining ecosystem functioning associated with reduced ecosystem services e.g., carbon sequestration and water retention, to harvest failure with very direct impacts on human wellbeing. In the last few years, it has been recognized that the highest threats on ecosystems and societies are due to multi-hazard events. Such events may translate into “compound events”, which often do not only affect one particular land-surface process but rather induce entire cascades of consequences.

In the project we will rely on deep learning to deal with spatio-temporal data, techniques from computer vision for forecasting impacts, and the advanced regression methods for associating impacts on biosphere and society. Understanding what the DL models have learned are of importance here, so experience on explainable AI (XAI) techniques and methods from modern Bayesian inference (Bayesian deep learning and deep GP regression) to perform uncertainty quantification, automatic variable relevance determination, and error propagation are pluses.

Profile ----------------------- - Experience in machine learning, deep learning, image processing, statistics, Bayesian inference - We love interdisciplinarity! Interested in remote sensing, Earth sciences, climate science

- Experienced in scientific interpretation and analysis of data - Experienced with or convincing motivation to enter a leadership position - Familiar with modelling, model-data-fusion and machine learning

- Excellent quantitative skills (e.g. data analysis, modelling) - Strong programming skills in Python/Julia/R - Proven record of scientific publications in international peer-reviewed journals

- PhD in maths, physics, or computer science - Critical and organized sense for data analysis - Maturity and commitment

- Strong communication, presentation and writing skills are a big plus - Collaborative team player

Your tasks ----------------------- - Lead research and development in the project activities

- Responsibility to deliver and report

- Collaborate and coordinate a team of researchers

- Not mandatory, but possibly to co-advise a PhD student

- Publish in international peer-reviewed journals and conference venues

APPLICATION ----------------------- - More information, conditions, and application form through http://isp.uv.es/openings.

- For enquiries, do not hesitate to contact Prof. Camps-Valls via email: [email protected]

Prof. Gustau Camps-Valls, IEEE Fellow, ELLIS Fellow Image Processing Laboratory (IPL) - Building E4 - Floor 4 Universitat de València C/ Catedrático José Beltrán, 9 46980 Paterna (València). Spain phone : +34 963 544 064 web : http://isp.uv.es e-mail : [email protected] twitter : http://twitter.com/isp_uv_es

Looking for Research Scientists and Research Engineers for research topics in AI for Healthcare

We have open positions for research scientists and research engineers at the University of Strasbourg & IHU Strasbourg Hospital.

We are looking for research scientists to conduct novel research in AI for healthcare in the areas of medical image analysis and computer aided surgery. The successful candidates will contribute to developing new areas of research at IHU Strasbourg and mentor a growing team of PhD students and engineers. Salary is competitive and permanent contracts will be offered to strong candidates with experience.

- We are looking for experienced research engineers in the areas of medical image analysis, computer aided surgery and federated learning to support our research team.

- We also have multiple internship positions in computer vision and machine learning for healthcare.

The successful candidates will be hosted within the AI team at IHU Strasbourg/University of Strasbourg, an institute offering an international environment with state-of-the-art computing resources and unique clinical facilities for both patients and medical research. More information about the positions is available here.

Nicolas Padoy Professor of Computer Science, University of Strasbourg Director of Computer Science and AI Research, IHU Strasbourg Head of Research Group CAMMA University of Strasbourg / ICube IHU Strasbourg, 1 place de l'Hôpital 67000 Strasbourg, France Web: http://camma.u-strasbg.fr Phone: +33 (0) 3 904 13530

A Phd position covering Deep Learning, color Science, Photonics and Laser Processing is available at the University of Saint-Etienne on:

*Prediction of multidimensional colors printed by laser on plasmonic metamaterials using deep learning and adaptive strategies*

- link: https://laboratoirehubertcurien.univ-st-etienne.fr/en/teams/functional-materials-and-surfaces/job-opportunities/phd-offer-prediction-of-multidimensional-colors-printed-by-laser-on-plasmonic-metamaterials-using-deep-learning-and-adaptive-strategies.html

- pdf document: https://laboratoirehubertcurien.univ-st-etienne.fr/_attachment/phd-offer-prediction-of-multidimensional-colors-printed-by-laser-on-plasmonic-metamaterials-using-deep-learning-and-adaptive-strategies-article/PhD%20offer%202022.pdf?download=true

- Application deadline: May 1st, 2022

Amaury Habrard Universite Jean Monnet de Saint-Etienne Laboratoire Hubert Curien - UMR CNRS 5516 18 rue du Professeur Lauras , 42000 Saint-Etienne Cedex 2 - France

Tel: (33) (0)4 69 66 32 64

e-mail: [email protected]

PhD subject: Natural Language Processing approaches in the musical domain: suitability, performance and limits

Application deadline: March 30th 2022

Details and contact: http://algomus.fr/jobs/phd-nlp-en/

Context In the last ten years, deep neural networks have intensely been investigated in the field of Natural Language Processing (NLP). This research has led to multiple applications including automated corpus annotation and content generation.

The temporal nature of music promotes its representation as sequences of elements, most commonly musical notes, that are comparable to sequences of words in NLP. This sequential point of view as well as the common assimilation of music to some kind of language, have motivated the use of methods originally designed for NLP, for symbolic Music Information Retrieval (MIR) tasks, including musical analysis and generation.

Objectives The main goal of this research will be to evaluate the adaptability, performance and relevance of NLP techniques when applied to the symbolic musical domain. We will in particular focus on the three following principles:

* self-attention * tokenization * transfer learning

These principles will be investigated through the lens of the structural and epistemological differences existing between natural language and music.

Profile of the candidate

Master’s degree (or equivalent) in computer science, machine learning, Natural Language Processing.

Musical knowledge and practice would be preferable.

Louis Bigo Université de Lille, CRIStAL, Algomus https://louisbigo.com