Boston AI Institute

Pay After Placement

ML Applications in Transportation Engineering - DEADLINE 31 August 2022
Transportation systems are complex, diverse, and dynamic in nature and operation. Researchers and practitioners have recently been faced with difficulties in obtaining comprehensive and current data needed to tackle rapidly emerging challenges, such as congestion of infrastructures, safety problems, environmental impacts, energy dependency, and social equity. With the rapid digitization and implementation of sensors in transport systems (e.g., personal devices, vehicles, infrastructures—including streets and sidewalks at the urban scale), there is a substantial wealth of data related to transport complex phenomena. Due to their advanced computation and data collection processes, machine learning is a fast and powerful tool that breaks down such complex problems into more straightforward and manageable mathematical operations. Researchers have developed machine learning methods to approach more traditional and novel transportation research problems with varying levels of success.

Machine learning encompasses many methodologies (e.g., supervised learning, unsupervised learning, reinforcement learning, and self-supervised learning, among others) and models (e.g., deep learning, support vector machines, decision trees, and evolutionary algorithms, among others) to explore new data sources and applications. Besides their improved performance compared to more conventional methods, machine learning could evolve to support planning and policy making in the transport field and, therefore, achieve more interpretable models and results (i.e., explainable artificial intelligence).

This Special Issue aims to collect and report new and innovative applications of machine learning methods to solve challenges presented by transportation systems. The scope of the research is diverse; topics of interest include, but are not limited to, the application of machine learning in various transportation fields and the following topics:

- Safety of transport infrastructures, particularly road users and vulnerable road users (pedestrians, cyclists, and scooter users);

- Monitoring, operation control, and management of mobility services, including shared-mobility services, public transportation management, Mobility-as-a-Service (MaaS), etc.;

- Intelligent transportation systems;

- Smart city logistics and micro-logistics;

- Management of public space management at the urban scale, including the intermittent and dynamic usage of road carriageways;

- Case studies in which machine learning was effectively used to make transportation systems more effective;

- Comparison of different approaches of machine learning methods with conventional approaches;

We welcome both original research and review articles. All submissions will be peer-reviewed according to the high standards of the journal.

Dr. Filipe Moura
Dr. Manuel Marques
Guest Editors

Deadline for manuscript submissions: 31 August 2022.

More info: https://www.mdpi.com/journal/applsci/special_issues/Machine_Learning_Applications_Transportation_Engineering

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bostonaiinstitute.com

Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czechia, EU invites applications for two-year postdoctoral positions in the institute
beginning in January 2023 with possibility to move to the tenure track in the institute.

Candidates are expected to work in one of these areas:
• artificial intelligence and machine learning,
• probabilistic graphical models,
• statistics and stochastics,
• image, video, and signal processing,
• control theory,
• adaptive decision intelligence and human-centric intelligence,
• modelling economic and financial problems,
• non-smooth analysis,
• PDEs, calculus of variations, and continuum mechanics.

The candidates are also expected to have a strong record of, or outstanding potential for, significant research and have no more than two years since being awarded a PhD, Dr. or equivalent title (as of September 30). Moreover, experience in obtaining third-party funds is advantageous.

The Institute offers a monthly salary of CZK 50 000 (about 2 000 EURO) and yearly benefits supporting e.g. recreational and sport activities, as well as health care programs. Complete applications must be received by August 31, 2022.

In case of interest, please send your application via email to ut....cas.cz .
The application should include a CV , a research statement, a motivation letter, and
a copy of the PhD diploma. Letter(s) of recommendation is/are welcome. They should be sent by their authors directly to the email above.

For further information see http://www.utia.cas.cz/news/3572
and the institute page at http://www.utia.cas.cz/

Jirka Vomlel

A research grant is available at the University of Salento, Lecce, Italy.
The research shall be developed in close collaboration with Echolight (https://www.echolightmedical.com, 12 months out of 18 in total), and is related to the following topic:

The main goal of the project is to improve the tuning and calibration process of noninvasive diagnostic imaging devices used for imaging. One of the most critical steps during the implementation of a diagnostic imaging device is its calibration. In fact, poor calibration can lead to unreliable instrument performance with noisy images and the presence of unwanted artefacts that could mislead the diagnosis made by the physician. The calibration phase involves a repeated try-and-check procedure during which the instrument parameters are repeatedly changed in order to obtain images that are sharp and as closely matched as possible to the target reference. This phase often requires considerable time expenditure and expert supervision; moreover, if one considers that calibration is carried out both following the production of the diagnostic instrument but also after several months of its use in the operational context, it is easy to deduce that automating this process on the one hand would improve the diagnostic yield, and on the other hand would reduce downtime and recalibration. The project aims to improve and automate the calibration process by introducing machine learning techniques for image classification. The results of the project find application on all instruments used for imaging, whether they are based on MRI, computed tomography, X-ray or ultrasound techniques. In fact, the goal is to relate the configuration parameters of the instrument to the images it produces in order to eliminate noise and artefacts produced by misconfiguration. Despite this, in the project we will consider as a case study the images produced by an ultrasound-based device produced by Echolight S.p.A. Medical devices produced by Echolight S.p.A. exploit images derived from ultrasound scans (B-Mode) to automatically identify anatomical reference targets (lumbar vertebrae bone interfaces of the L1-L4 tract and proximal femur bone interface). Once the regions of interest (ROIs) are identified, a proprietary algorithm evaluates the spectral characteristics of selected portions of the raw ultrasound signal related to the analyzed bone tissues. From the analysis of the raw signal characteristics, a measure of the bone mineral density (BMD) of the analyzed anatomical sites is determined. In order to provide reliable, repeatable, and accurate BMD measurements, special calibration and testing procedures have been developed, however, which require several manual measurements and checks, resulting in a high human-time commitment and, consequently, introducing a risk of human error on the collection and interpretation of the collected measurements and results. Leveraging the image processing and image classification techniques developed within the project, the algorithm will provide output indicative of the presence of artefacts or other alterations in the performance of the ultrasound system in production in order to possibly intervene with further modifications and calibrations. As part of the project, standard conditions for conducting tests will also be defined through the use of specific ultrasound phantoms provided by the company.

Prof. Italo Epicoco ([email protected]) is the scientific responsible for this research grant.

DEADLINE: June 24, 2022
ALL INCLUSIVE GROSS AMOUNT (for 18 months): 29050,50 euro (i.e., 19367 euro annual gross amount)

NOTE: Foreign candidates are strongly encouraged to contact Prof. Epicoco by email if they need help/support in order to prepare their application: he will be glad to assist.

Prof. Massimo Cafaro, Ph.D.
Associate Professor of Parallel Algorithms and Data Mining/Machine Learning
Head of HPC Lab https://hpc-lab.unisalento.it
Director of Master in Applied Data Science

Department of Engineering for Innovation
University of Salento, Lecce, Italy
Via per Monteroni
73100 Lecce, Italy

Voice/Fax +39 0832 297371

Web https://www.massimocafaro.it
Web https://www.unisalento.it/people/massimo.cafaro

E-mail [email protected]
E-mail [email protected]
E-mail [email protected]

INGV
National Institute of Geophysics and Volcanology
Via di Vigna Murata 605
Roma

CMCC Foundation
Euro-Mediterranean Center on Climate Change
Via Augusto Imperatore, 16 - 73100 Lecce
[email protected]

unisalento.it

PhD Position Research Associate/PhD Candidate “Computer Science: Computational Linguistics & Corpus Annotation” (m/f/d), TIB – Leibniz Information Centre for Science and Technology, Germany

University or Organization: TIB – Leibniz Information Centre for Science and Technology and University Library
Department: Data Science and Digital Libraries
Job Location: Hannover, Germany
Web Address: https://www.tib.eu/en/research-development/research-groups-and-labs/data-science-digital-libraries
Job Title: Research Associate/PhD Candidate

Job Rank: PhD Candidate

Specialty Areas: Natural Language Processing; Computational Linguistics; Corpus Annotation

Description:
The PhD topics will be in the context of the Open Research Knowledge Graph (https://www.orkg.org) and the project “SCINEXT - Neural-Symbolic Scholarly Innovation Extraction”, funded by the Federal Ministry of Education and Research (BMBF). The aim of these projects is to research and develop techniques for crowdsourcing, representing and managing semantically structured, rich representations of scholarly contributions and research data in knowledge graphs and thus develop a novel model for scholarly communication. In the context of the PhD thesis you will be responsible for conducting independent and original scientific research involving corpus development and annotation in organizing research contributions in the ORKG in a structured, semantic way, so other researchers can get a quick overview on the state-of-the-art in the field. You will participate in local, national and international collaboration activities. Given the multidisciplinary nature of the program, we encourage applicants with a strong curiosity and interest in Science to apply.

The tasks will focus on

Collaborating with researchers from different disciplines to gain familiarity with research problems and their contribution descriptions expressed in scholarly literature.
Conceptually designing, modelling and implementing ontology-based knowledge representations for crowdsourcing of the Open Research Knowledge Graph.
Annotation and curation of multidisciplinary scholarly contribution descriptions.

Application Deadline: Open until filled

Web Address for Applications: https://www.tib.eu/en/tib/careers-and-apprenticeships/vacancies/details/stellenausschreibung-nr-32-2022
Contact Information:
Dr. Jennifer D'Souza
Email: jennife....eu

Job advertisement No. 32/2022 Research Associate/PhD Candidate “Computer Science: Computational Linguistics & Corpus Annotation” (m/f/d)

PhD Position “Computer Science: Natural Language Processing & Semantic Web Technologies” (m/f/d), TIB – Leibniz Information Centre for Science and Technology, Germany

University or Organization: TIB – Leibniz Information Centre for Science and Technology and University Library
Department: Data Science and Digital Libraries
Job Location: Hannover, Germany
Web Address: https://www.tib.eu/en/research-development/research-groups-and-labs/data-science-digital-libraries
Job Title: Research Associate/PhD Candidate

Job Rank: PhD Candidate

Specialty Areas: Natural Language Processing; Computational Linguistics; Software Engineering; Computer Science

Description:
The PhD topics will be in the context of the Open Research Knowledge Graph (https://www.orkg.org) and the project “SCINEXT - Neural-Symbolic Scholarly Innovation Extraction”, funded by the Federal Ministry of Education and Research (BMBF). The aim of these projects is to research and develop techniques for crowdsourcing, representing and managing semantically structured, rich representations of scholarly contributions and research data in knowledge graphs and thus develop a novel model for scholarly communication. In the context of the PhD thesis you will be responsible for building and maintaining the ORKG data ingestion and processing pipelines to ensure the flow of high-quality semantified resources from publications. Your main responsibility in this position will be to build scalable solutions that crawl, ingest, process publications, and thereby enrich the ORKG. You will work alongside the ORKG engineering team to set up the AI/NLP ecosphere.

Your tasks will focus on

Working in the areas of Natural Language Processing (text mining, information extraction, information retrieval/search) and Machine Learning of scholarly communication media (digital) data.
Identifying and implementing the tools and algorithms appropriate for Natural Language Processing assignments to enhance the NLP system currently in place.
Conceptually designing, modeling, and implementing data-driven services for information retrieval and extraction, data enrichment, and linking of data.
Carrying out evaluation experiments and training the developed model.

Application Deadline: Open until filled

Web Address for Applications: https://www.tib.eu/en/tib/careers-and-apprenticeships/vacancies/details/stellenausschreibung-nr-31-2022
Contact Information:
Dr. Jennifer D'Souza
Email: jennife....eu

Job advertisement No. 31/2022 Research Associate/PhD Candidate “Computer Science: Natural Language Processing & Semantic Web Technologies” (m/f/d)

We are looking for excellent candidates to fill two PhD positions that are currently available at the University of Namur in the field of artificial intelligence, financed by the ARIAC project. The two positions focus on the following research topics:
Language games for ontology alignment under partial observability
Computational construction grammar and procedural semantics for multi-modal, linguistic, human-machine interaction
The deadline for applying for these positions is July 1st 2022. Soon-to-be graduating master students are welcome to apply provided that they will have graduated before the start of the position. A more detailed description of the project and the application procedure is included here below.

Thank you for helping us spread the word.

Best wishes,
Katrien Beuls, Anthony Cleve and Bruno Dumas

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Title
Full-time PhD researcher in artificial intelligence - Université de Namur
Language games for ontology alignment under partial observability (ARIAC project)
https://jobs.unamur.be/emploi.2022-05-17.7380520887
https://euraxess.ec.europa.eu/jobs/785563

Project
ARIAC by DigitalWallonia4.ai is a research project funded by the Walloon Region bringing together the five French-speaking universities and four Walloon research centres with the primary objective of accelerating the development of artificial intelligence technologies in Wallonia. The project is part of the TRAIL (Trusted AI Labs) initiative, launched in September 2020, which aims at enhancing the development of artificial intelligence technologies in Wallonia.

In the context of this position, you will investigate how a population of agents can autonomously develop a new language for exchanging information. The information held by each agent might be stored in entirely different formats and according to different ontologies. Through task-based communicative interactions, the agents invent, adopt and align the building blocks of an emergent language. This language can then be used by all agents in the population to exchange information, as it forms an abstraction layer over the individual ontological organisations and storage formats.

You will carry out this PhD project both individually and in collaboration with other researchers in the field. It is conceived as a 4-year program, starting with a 2-year contract that is renewable for up to 2 additional years. You will be supervised by Prof. dr. K. Beuls and Prof. dr. A. Cleve.

Profile
You have obtained a master’s degree in artificial intelligence, computer science, linguistics, mathematics or a related domain. You have a solid academic track record.
You are passionate about building intelligent systems that are capable of interacting with their users and environment through natural language.
You strive for excellence and have a scientific mindset.
You are a loyal team player, who can work autonomously and deliver solid scientific work.
You have strong communication skills and a good command of English. Knowledge of French is considered as a plus.
Additional information
For additional information please do not hesitate to contact Prof. K. Beuls or Prof. A. Cleve (see addresses below).

Important dates
Submission deadline: July 1st, 2022 (11h59 pm AoE).
Expected starting date: September 1st, 2022.

How to apply?
Applications should be sent by e-mail to secretar....be, katrie....be AND anthon....be and contain the following:
A motivation letter describing your interest in the research topic
Your CV
A copy of your diplomas (Bachelor and Master, if available)
A transcript with the grades you obtained for each course taken on each university year
The name and e-mail address of two references to be contacted upon request

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Title
Full-time PhD researcher in artificial intelligence - Université de Namur
Computational construction grammar and procedural semantics for multi-modal, linguistic, human-machine interaction (ARIAC project)
https://jobs.unamur.be/emploi.2022-05-17.2794384091
https://euraxess.ec.europa.eu/jobs/785573

Project
ARIAC by DigitalWallonia4.ai is a research project funded by the Walloon Region bringing together the five French-speaking universities and four Walloon research centres with the primary objective of accelerating the development of artificial intelligence technologies in Wallonia. The project is part of the TRAIL (Trusted AI Labs) initiative, launched in September 2020, which aims at enhancing the development of artificial intelligence technologies in Wallonia.

Current techniques for linguistic human-machine interaction focus on either speech signals, textual data or gestures. However, communication between humans is multi-modal in nature. In particular, spoken utterances are accompanied by gestures, pointing, eye gaze, facial expressions, and prosody amongst others. These multi-modal channels are of great importance to supporting the basic function of language, i.e. exchanging information between individuals. A great challenge for human-machine interaction resides thus in the integration of multi-modal signals into a single device for comprehending and producing natural language expressions. Within the ARIAC project, we will develop a novel methodology for multi-modal language comprehension and production. This methodology will be situated within the framework of computational construction grammar, a family of linguistic theories which is well-suited to accommodate the representation and processing of linguistic knowledge that combines different sources of information. While the representation of the form of linguistic expressions will consist in abstractions over data captured through different feature channels, the representation of the meaning of linguistic expressions will be formalised using procedural semantics. In this way, the information captured in the multi-modal linguistic expressions can be executed by a machine, or a machine can produce multi-modal expressions based on procedural semantic representations. The primary application domain of this research will be systems for multi-modal human-machine interaction, i.e. systems where humans can give instructions to a machine by simultaneously speaking and gesturing.

You will carry out this PhD project both individually and in collaboration with other researchers in the field. It is conceived as a 4-year program, starting with a 2-year contract that is renewable for up to 2 additional years. You will be supervised by Prof. dr. K. Beuls and Prof. dr. B. Dumas.

Profile
You have obtained a master’s degree in artificial intelligence, computer science, linguistics, mathematics or a related domain. You have a solid academic track record.
You are passionate about building intelligent systems that are capable of interacting with their users and environment through natural language.
You strive for excellence and have a scientific mindset.
You are a loyal team player, who can work autonomously and deliver solid scientific work.
You have strong communication skills and a good command of English. Knowledge of French is considered as a plus.
Additional information
For additional information please do not hesitate to contact Prof. K. Beuls or Prof. B. Dumas (see addresses below).

Important dates
Submission deadline: July 1st, 2022 (11h59 pm AoE).
Expected starting date: September 1st, 2022.

How to apply?
Applications should be sent by e-mail to secretar....be, katrie....be AND bruno....be and contain the following:
A motivation letter describing your interest in the research topic
Your CV
A copy of your diplomas (Bachelor and Master, if available)
A transcript with the grades you obtained for each course taken on each university year
The name and e-mail address of two references to be contacted upon request

Full-time PhD student in AI - Computational construction grammar and procedural semantics for multi-modal, linguistic, human-machine interaction (ARIAC project) About the employer The University of Namur (UNamur) is located in the centre of Belgium, in the French-speaking part of the country. It offers quality education to more than 7,000 students every year and hosts more than 900 researchers in all fields of expertise. The Faculty of Computer Science prov...

CALL FOR APPLICANTS
Postdoctoral research fellow
Interuniversity research center on Integrated System for the Marine Environment (www.isme.unige.it) at the University of Genoa (www.unige.it)
1 year duration (with possibility of renewal)
pre-tax amount: € 23250

Job Description

The Interuniversity research center on Integrated System for the Marine Environment is accepting applications for the position of postdoctoral research fellow to start on 1st July 2022.
The research fellow will contribute to an ongoing project on the control of autonomous underwater vehicles operating in very shallow water areas. The objective of this research grant is the development of an obstacle detection and avoidance. The vehicle will be endowed with a forward-looking sonar and a camera. First, it will be necessary to develop image processing and machine learning algorithms for both the acoustic and optical images, with the goal of detecting the position of the obstacles. Then, the research will focus on the development of control strategies to avoid the detected obstacles.

Qualifications

Applicants should have a Ph.D. in a closely related engineering discipline (e.g., robotics, computer science, computer engineering). The postdoctoral associate must have excellent oral and written communication skills and a strong interest in marine robotics, control & perception. The postdoctoral research fellow must have expertise/experience in control engineering and robotics, including:
- feedback control theory
- object oriented programming in C++
- knowledge of ROS and/or ROS2
- computer vision
- machine learning

About the institution

The interuniversity research center on Integrated System for the Marine Environment (ISME) collects nine Italian university operating in the field of marine robotics. It has been funded in 1999 and has been ever since very active at the European level. The University of Genoa is the legal headquarters of ISME. Recent projects carried out by ISME are the WiMUST project, on autonomous geotechnical surveying with a team of marine robots (https://cordis.europa.eu/project/id/645141), the DexROV project, on semi-autonomous teleoperation of ROVs from remote locations (https://www.dexrov.eu/) and the ROBUST project, on deep sea mining exploration with AUVs (http://eu-robust.eu/).

Application process

The call, explaining the application process, is reported here
https://concorsi.unige.it/home/procedure/3290/?downloadAttachment=&code=ftv547cnpkfgzlv0x6vbdrc51
Specifically, the program is the number 28.

The application can be submitted at:
https://concorsi.unige.it/home/procedure/3317

D.R. n. 1724 del 22.4.2022 Programma n. 28

Open PhD Positions AI & Data Analytics (AIDA) Group @ Ghent University

The AIDA group is seeking motivated candidates for up to three fully funded four-year PhD student positions in Artificial Intelligence, Machine Learning, and Data Visualization. Candidates with a PhD that are interested in a postdoc position, please do also contact us.

Our group conducts internationally top research in AI in the areas of machine learning and knowledge discovery. An overview of our group and research portfolio can be found at https://aida.ugent.be. We are looking for candidates for the following projects:

Machine learning on knowledge graphs. This project aims to overcome challenges created by both the diversity present in knowledge graphs and by their distributed nature, in comparison to current machine learning methods for graphs. Collaboration with the semantic web group and the recently founded SOLID lab Flanders.

Machine learning & interactive visualization for single-cell data. Single-cell data allows us to study biological processes in detail. This project aims to enable more generic use of expert knowledge in machine learning and visualization for biological data. Collaboration with the DAMBI research group (VIB & Ghent University).

Machine learning for the job market. People are all unique and perhaps jobs are too. This and other aspects make the job market a difficult setting, for example for recommenders. This project aims to better understand and tackle the challenges of applying machine learning in the job market. Collaboration with the VDAB (Flanders employment agency).

Funding comes from the ERC, FWO, and the Flanders AI Research programme.

Your profile

You will need to hold a master’s degree in a relevant area with demonstrated first-class performance (e.g., outstanding grades, thesis result, or publications). Ideal candidates demonstrate mastery of the core aspects of machine learning:

● Fundamentals of probability and statistics

● Algorithm design, discrete/continuous optimisation

● Programming (in Python, C++/Java, JavaScript, …)

Experience with data visualization, user interfaces, and/or cell biology is a plus.

The research environment

The research will be supervised by Prof. Tijl De Bie and Prof. Jefrey Lijffijt of AIDA, and the single-cell data project also by Prof. Yvan Saeys (https://www.dambi.ugent.be/). We are engaging international teams with a track record in world-leading research. We are part of the Ghent University IDLab and the cross-university Flanders AI Research initiative.

Ghent University is a comprehensive internationally leading research-intensive university. English is the working language; knowledge of Dutch or French is not required. The university also offers a wide range of courses for skills professionalization. Ghent is a historical, vibrant, and internationally minded city in Belgium with 260k inhabitants.

Further info & application procedure

Applications may be sent by email to jefrey.lijffijt at ugent.be. Please include a motivation letter, CV, and contact details of 1 to 3 referees. Do not include reference letters, these will be solicited by us. Please mention which project you are interested in. Suitable candidates will be invited for an interview. Please apply by Tuesday May 31, 2022. Late applications can be considered but the posts may already be filled.

IRC: Yvan SAEYS Our group studies the design and application of novel data mining and machine learning techniques, motivated by specific questions in biology and medicine.  To this end, our group combines the expertise of both strong analytical skills, exemplified by solid backgrounds in applied mathematics, compu...

The Math & Algorithms Group at Nokia Bell Labs is looking for a full-time machine learning researcher at Murray Hill, NJ. The group performs research in a spectrum of topics that combine foundational work on algorithms with novel solutions for challenging optimization, classification, and decision problems. Our recent work includes the development of quantum error correcting codes, MIMO audio echo cancellers, random matrix theory, massive MIMO systems, structure learning, (deep) autoencoders, customized language models, depth separation in DNNs and numerous novel applications.

What you will learn and contribute to
Design and develop algorithms for new communication and machine learning applications
Develop information theoretic bounds for various classification and regression tasks in both supervised and unsupervised learning contexts
Help make AI/ML methods better aligned with theory
Create novel applications of AI/ML tools to communication problems

Your skills and experience
A PhD in CS, EE, Applied Math, OR and related fields
Ability to write papers and make presentations of highly technical material
Interest in a broad collection of topics
Experience in applying theoretical tools to practical problems
Experience in collaborative research
Keen to learn new methods and tools
Familiarity with basic tools such as Matlab, Mathematica, Python scripts/libraries, ML tools such as Tensorflow, etc.
For details and to apply, please go to https://careers.nokia.com/jobs/machine-learning-algorithms-researcher-79501